Philipp Koehn


2021

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Learning Curricula for Multilingual Neural Machine Translation Training
Gaurav Kumar | Philipp Koehn | Sanjeev Khudanpur
Proceedings of Machine Translation Summit XVIII: Research Track

Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs with the aid of high-resource language pairs. In this paper and we propose two simple search based curricula – orderings of the multilingual training data – which help improve translation performance in conjunction with existing techniques such as fine-tuning. Additionally and we attempt to learn a curriculum for MNMT from scratch jointly with the training of the translation system using contextual multi-arm bandits. We show on the FLORES low-resource translation dataset that these learned curricula can provide better starting points for fine tuning and improve overall performance of the translation system.

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An Alignment-Based Approach to Semi-Supervised Bilingual Lexicon Induction with Small Parallel Corpora
Kelly Marchisio | Philipp Koehn | Conghao Xiong
Proceedings of Machine Translation Summit XVIII: Research Track

Aimed at generating a seed lexicon for use in downstream natural language tasks and unsupervised methods for bilingual lexicon induction have received much attention in the academic literature recently. While interesting and fully unsupervised settings are unrealistic; small amounts of bilingual data are usually available due to the existence of massively multilingual parallel corpora and or linguists can create small amounts of parallel data. In this work and we demonstrate an effective bootstrapping approach for semi-supervised bilingual lexicon induction that capitalizes upon the complementary strengths of two disparate methods for inducing bilingual lexicons. Whereas statistical methods are highly effective at inducing correct translation pairs for words frequently occurring in a parallel corpus and monolingual embedding spaces have the advantage of having been trained on large amounts of data and and therefore may induce accurate translations for words absent from the small corpus. By combining these relative strengths and our method achieves state-of-the-art results on 3 of 4 language pairs in the challenging VecMap test set using minimal amounts of parallel data and without the need for a translation dictionary. We release our implementation at www.blind-review.code.

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Evaluating Saliency Methods for Neural Language Models
Shuoyang Ding | Philipp Koehn
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model. In these cases, how do we identify when are these interpretations trustworthy enough to be used in analyses? To address this question, we conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models. We evaluate the quality of prediction interpretations from two perspectives that each represents a desirable property of these interpretations: plausibility and faithfulness. Our evaluation is conducted on four different datasets constructed from the existing human annotation of syntactic and semantic agreements, on both sentence-level and document-level. Through our evaluation, we identified various ways saliency methods could yield interpretations of low quality. We recommend that future work deploying such methods to neural language models should carefully validate their interpretations before drawing insights.

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Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
Wei-Jen Ko | Ahmed El-Kishky | Adithya Renduchintala | Vishrav Chaudhary | Naman Goyal | Francisco Guzmán | Pascale Fung | Philipp Koehn | Mona Diab
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this work, we exploit this linguistic overlap to facilitate translating to and from a low-resource language with only monolingual data, in addition to any parallel data in the related high-resource language. Our method, NMT-Adapt, combines denoising autoencoding, back-translation and adversarial objectives to utilize monolingual data for low-resource adaptation. We experiment on 7 languages from three different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines.

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Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation
Haoran Xu | Philipp Koehn
Proceedings of the Second Workshop on Domain Adaptation for NLP

Linear embedding transformation has been shown to be effective for zero-shot cross-lingual transfer tasks and achieve surprisingly promising results. However, cross-lingual embedding space mapping is usually studied in static word-level embeddings, where a space transformation is derived by aligning representations of translation pairs that are referred from dictionaries. We move further from this line and investigate a contextual embedding alignment approach which is sense-level and dictionary-free. To enhance the quality of the mapping, we also provide a deep view of properties of contextual embeddings, i.e., the anisotropy problem and its solution. Experiments on zero-shot dependency parsing through the concept-shared space built by our embedding transformation substantially outperform state-of-the-art methods using multilingual embeddings.

2020

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ParaCrawl: Web-Scale Acquisition of Parallel Corpora
Marta Bañón | Pinzhen Chen | Barry Haddow | Kenneth Heafield | Hieu Hoang | Miquel Esplà-Gomis | Mikel L. Forcada | Amir Kamran | Faheem Kirefu | Philipp Koehn | Sergio Ortiz Rojas | Leopoldo Pla Sempere | Gema Ramírez-Sánchez | Elsa Sarrías | Marek Strelec | Brian Thompson | William Waites | Dion Wiggins | Jaume Zaragoza
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the parallel corpora released and evaluate their quality and their usefulness to create machine translation systems.

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Proceedings of the Fifth Conference on Machine Translation
Loïc Barrault | Ondřej Bojar | Fethi Bougares | Rajen Chatterjee | Marta R. Costa-jussà | Christian Federmann | Mark Fishel | Alexander Fraser | Yvette Graham | Paco Guzman | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Makoto Morishita | Christof Monz | Masaaki Nagata | Toshiaki Nakazawa | Matteo Negri
Proceedings of the Fifth Conference on Machine Translation

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Findings of the 2020 Conference on Machine Translation (WMT20)
Loïc Barrault | Magdalena Biesialska | Ondřej Bojar | Marta R. Costa-jussà | Christian Federmann | Yvette Graham | Roman Grundkiewicz | Barry Haddow | Matthias Huck | Eric Joanis | Tom Kocmi | Philipp Koehn | Chi-kiu Lo | Nikola Ljubešić | Christof Monz | Makoto Morishita | Masaaki Nagata | Toshiaki Nakazawa | Santanu Pal | Matt Post | Marcos Zampieri
Proceedings of the Fifth Conference on Machine Translation

This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

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Findings of the WMT 2020 Shared Task on Machine Translation Robustness
Lucia Specia | Zhenhao Li | Juan Pino | Vishrav Chaudhary | Francisco Guzmán | Graham Neubig | Nadir Durrani | Yonatan Belinkov | Philipp Koehn | Hassan Sajjad | Paul Michel | Xian Li
Proceedings of the Fifth Conference on Machine Translation

We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs – English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for ”catastrophic errors”. We received 59 submissions by 11 participating teams from a variety of types of institutions.

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When Does Unsupervised Machine Translation Work?
Kelly Marchisio | Kevin Duh | Philipp Koehn
Proceedings of the Fifth Conference on Machine Translation

Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which the methods succeed and fail. We conduct an extensive empirical evaluation using dissimilar language pairs, dissimilar domains, and diverse datasets. We find that performance rapidly deteriorates when source and target corpora are from different domains, and that stochasticity during embedding training can dramatically affect downstream results. We additionally find that unsupervised MT performance declines when source and target languages use different scripts, and observe very poor performance on authentic low-resource language pairs. We advocate for extensive empirical evaluation of unsupervised MT systems to highlight failure points and encourage continued research on the most promising paradigms. We release our preprocessed dataset to encourage evaluations that stress-test systems under multiple data conditions.

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Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment
Philipp Koehn | Vishrav Chaudhary | Ahmed El-Kishky | Naman Goyal | Peng-Jen Chen | Francisco Guzmán
Proceedings of the Fifth Conference on Machine Translation

Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems. This year, the task tackled the low resource condition of Pashto–English and Khmer–English and also included the challenge of sentence alignment from document pairs.

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An exploratory approach to the Parallel Corpus Filtering shared task WMT20
Ankur Kejriwal | Philipp Koehn
Proceedings of the Fifth Conference on Machine Translation

In this document we describe our submission to the parallel corpus filtering task using multilingual word embedding, language models and an ensemble of pre and post filtering rules. We use the norms of embedding and the perplexities of language models along with pre/post filtering rules to complement the LASER baseline scores and in the end get an improvement on the dev set in both language pairs.

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Dual Conditional Cross Entropy Scores and LASER Similarity Scores for the WMT20 Parallel Corpus Filtering Shared Task
Felicia Koerner | Philipp Koehn
Proceedings of the Fifth Conference on Machine Translation

This paper describes our submission to the WMT20 Parallel Corpus Filtering and Alignment for Low-Resource Conditions Shared Task. This year’s corpora are noisy Khmer-English and Pashto-English, with 58.3 million and 11.6 million words respectively (English token count). Our submission focuses on filtering Pashto-English, building on previously successful methods to produce two sets of scores: LASER_LM, a combination of the LASER similarity scores provided in the shared task and perplexity scores from language models, and DCCEF_DUP, dual conditional cross entropy scores combined with a duplication penalty. We improve slightly on the LASER similarity score and find that the provided clean data can successfully be supplemented with a subsampled set of the noisy data, effectively increasing the training data for the models used for dual conditional cross entropy scoring.

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Statistical Power and Translationese in Machine Translation Evaluation
Yvette Graham | Barry Haddow | Philipp Koehn
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The term translationese has been used to describe features of translated text, and in this paper, we provide detailed analysis of potential adverse effects of translationese on machine translation evaluation. Our analysis shows differences in conclusions drawn from evaluations that include translationese in test data compared to experiments that tested only with text originally composed in that language. For this reason we recommend that reverse-created test data be omitted from future machine translation test sets. In addition, we provide a re-evaluation of a past machine translation evaluation claiming human-parity of MT. One important issue not previously considered is statistical power of significance tests applied to comparison of human and machine translation. Since the very aim of past evaluations was investigation of ties between human and MT systems, power analysis is of particular importance, to avoid, for example, claims of human parity simply corresponding to Type II error resulting from the application of a low powered test. We provide detailed analysis of tests used in such evaluations to provide an indication of a suitable minimum sample size for future studies.

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Simulated multiple reference training improves low-resource machine translation
Huda Khayrallah | Brian Thompson | Matt Post | Philipp Koehn
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser’s distribution over possible tokens. We demonstrate the effectiveness of SMRT in low-resource settings when translating to English, with improvements of 1.2 to 7.0 BLEU. We also find SMRT is complementary to back-translation.

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CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs
Ahmed El-Kishky | Vishrav Chaudhary | Francisco Guzmán | Philipp Koehn
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.

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Exploiting Sentence Order in Document Alignment
Brian Thompson | Philipp Koehn
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring. Our method results in 61% relative reduction in error compared to the best previously published result on the WMT16 document alignment shared task. Our method improves downstream MT performance on web-scraped Sinhala–English documents from ParaCrawl, outperforming the document alignment method used in the most recent ParaCrawl release. It also outperforms a comparable corpora method which uses the same multilingual embeddings, demonstrating that exploiting sentence order is beneficial even if the end goal is sentence-level bitext.

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TICO-19: the Translation Initiative for COvid-19
Antonios Anastasopoulos | Alessandro Cattelan | Zi-Yi Dou | Marcello Federico | Christian Federmann | Dmitriy Genzel | Franscisco Guzmán | Junjie Hu | Macduff Hughes | Philipp Koehn | Rosie Lazar | Will Lewis | Graham Neubig | Mengmeng Niu | Alp Öktem | Eric Paquin | Grace Tang | Sylwia Tur
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations the means by which they can protect themselves. To this end, the collaborators forming the Translation Initiative for COvid-19 (TICO-19) have made test and development data available to AI and MT researchers in 35 different languages in order to foster the development of tools and resources for improving access to information about COVID-19 in these languages. In addition to 9 high-resourced, ”pivot” languages, the team is targeting 26 lesser resourced languages, in particular languages of Africa, South Asia and South-East Asia, whose populations may be the most vulnerable to the spread of the virus. The same data is translated into all of the languages represented, meaning that testing or development can be done for any pairing of languages in the set. Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.

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SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation
Xutai Ma | Juan Pino | Philipp Koehn
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

We investigate how to adapt simultaneous text translation methods such as wait-k and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies. We also design a novel computation-aware latency metric, adapted from Average Lagging.

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A Survey of Qualitative Error Analysis for Neural Machine Translation Systems
Denise Díaz | James Cross | Vishrav Chaudhary | Ahmed Kishky | Philipp Koehn
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)

2019

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Vecalign: Improved Sentence Alignment in Linear Time and Space
Brian Thompson | Philipp Koehn
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We introduce Vecalign, a novel bilingual sentence alignment method which is linear in time and space with respect to the number of sentences being aligned and which requires only bilingual sentence embeddings. On a standard German–French test set, Vecalign outperforms the previous state-of-the-art method (which has quadratic time complexity and requires a machine translation system) by 5 F1 points. It substantially outperforms the popular Hunalign toolkit at recovering Bible verse alignments in medium- to low-resource language pairs, and it improves downstream MT quality by 1.7 and 1.6 BLEU in Sinhala-English and Nepali-English, respectively, compared to the Hunalign-based Paracrawl pipeline.

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HABLex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation
Brian Thompson | Rebecca Knowles | Xuan Zhang | Huda Khayrallah | Kevin Duh | Philipp Koehn
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Bilingual lexicons are valuable resources used by professional human translators. While these resources can be easily incorporated in statistical machine translation, it is unclear how to best do so in the neural framework. In this work, we present the HABLex dataset, designed to test methods for bilingual lexicon integration into neural machine translation. Our data consists of human generated alignments of words and phrases in machine translation test sets in three language pairs (Russian-English, Chinese-English, and Korean-English), resulting in clean bilingual lexicons which are well matched to the reference. We also present two simple baselines - constrained decoding and continued training - and an improvement to continued training to address overfitting.

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The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English
Francisco Guzmán | Peng-Jen Chen | Myle Ott | Juan Pino | Guillaume Lample | Philipp Koehn | Vishrav Chaudhary | Marc’Aurelio Ranzato
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited supervision, it is difficult to evaluate methods trained on low-resource language pairs because of the lack of freely and publicly available benchmarks. In this work, we introduce the FLORES evaluation datasets for Nepali–English and Sinhala– English, based on sentences translated from Wikipedia. Compared to English, these are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low-resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores.

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Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary
Adithya Renduchintala | Philipp Koehn | Jason Eisner
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We present a machine foreign-language teacher that modifies text in a student’s native language (L1) by replacing some word tokens with glosses in a foreign language (L2), in such a way that the student can acquire L2 vocabulary simply by reading the resulting macaronic text. The machine teacher uses no supervised data from human students. Instead, to guide the machine teacher’s choice of which words to replace, we equip a cloze language model with a training procedure that can incrementally learn representations for novel words, and use this model as a proxy for the word guessing and learning ability of real human students. We use Mechanical Turk to evaluate two variants of the student model: (i) one that generates a representation for a novel word using only surrounding context and (ii) an extension that also uses the spelling of the novel word.

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Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation
Brian Thompson | Jeremy Gwinnup | Huda Khayrallah | Kevin Duh | Philipp Koehn
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Continued training is an effective method for domain adaptation in neural machine translation. However, in-domain gains from adaptation come at the expense of general-domain performance. In this work, we interpret the drop in general-domain performance as catastrophic forgetting of general-domain knowledge. To mitigate it, we adapt Elastic Weight Consolidation (EWC)—a machine learning method for learning a new task without forgetting previous tasks. Our method retains the majority of general-domain performance lost in continued training without degrading in-domain performance, outperforming the previous state-of-the-art. We also explore the full range of general-domain performance available when some in-domain degradation is acceptable.

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Parallelizable Stack Long Short-Term Memory
Shuoyang Ding | Philipp Koehn
Proceedings of the Third Workshop on Structured Prediction for NLP

Stack Long Short-Term Memory (StackLSTM) is useful for various applications such as parsing and string-to-tree neural machine translation, but it is also known to be notoriously difficult to parallelize for GPU training due to the fact that the computations are dependent on discrete operations. In this paper, we tackle this problem by utilizing state access patterns of StackLSTM to homogenize computations with regard to different discrete operations. Our parsing experiments show that the method scales up almost linearly with increasing batch size, and our parallelized PyTorch implementation trains significantly faster compared to the Dynet C++ implementation.

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Simple Construction of Mixed-Language Texts for Vocabulary Learning
Adithya Renduchintala | Philipp Koehn | Jason Eisner
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

We present a machine foreign-language teacher that takes documents written in a student’s native language and detects situations where it can replace words with their foreign glosses such that new foreign vocabulary can be learned simply through reading the resulting mixed-language text. We show that it is possible to design such a machine teacher without any supervised data from (human) students. We accomplish this by modifying a cloze language model to incrementally learn new vocabulary items, and use this language model as a proxy for the word guessing and learning ability of real students. Our machine foreign-language teacher decides which subset of words to replace by consulting this language model. We evaluate three variants of our student proxy language models through a study on Amazon Mechanical Turk (MTurk). We find that MTurk “students” were able to guess the meanings of foreign words introduced by the machine teacher with high accuracy for both function words as well as content words in two out of the three models. In addition, we show that students are able to retain their knowledge about the foreign words after they finish reading the document.

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Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Marco Turchi | Karin Verspoor
Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

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Saliency-driven Word Alignment Interpretation for Neural Machine Translation
Shuoyang Ding | Hainan Xu | Philipp Koehn
Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

Despite their original goal to jointly learn to align and translate, Neural Machine Translation (NMT) models, especially Transformer, are often perceived as not learning interpretable word alignments. In this paper, we show that NMT models do learn interpretable word alignments, which could only be revealed with proper interpretation methods. We propose a series of such methods that are model-agnostic, are able to be applied either offline or online, and do not require parameter update or architectural change. We show that under the force decoding setup, the alignments induced by our interpretation method are of better quality than fast-align for some systems, and when performing free decoding, they agree well with the alignments induced by automatic alignment tools.

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Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Marco Turchi | Karin Verspoor
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

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Findings of the 2019 Conference on Machine Translation (WMT19)
Loïc Barrault | Ondřej Bojar | Marta R. Costa-jussà | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Philipp Koehn | Shervin Malmasi | Christof Monz | Mathias Müller | Santanu Pal | Matt Post | Marcos Zampieri
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.

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Findings of the First Shared Task on Machine Translation Robustness
Xian Li | Paul Michel | Antonios Anastasopoulos | Yonatan Belinkov | Nadir Durrani | Orhan Firat | Philipp Koehn | Graham Neubig | Juan Pino | Hassan Sajjad
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models’ robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations. As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc. All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU. We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson’s r = 0.94 and 0.95). Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt, which revealed their salient differences in handling challenges in this task. Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g. systems better at producing colloquial expressions received higher score from human judgment.

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Johns Hopkins University Submission for WMT News Translation Task
Kelly Marchisio | Yash Kumar Lal | Philipp Koehn
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

We describe the work of Johns Hopkins University for the shared task of news translation organized by the Fourth Conference on Machine Translation (2019). We submitted systems for both directions of the English-German language pair. The systems combine multiple techniques – sampling, filtering, iterative backtranslation, and continued training – previously used to improve performance of neural machine translation models. At submission time, we achieve a BLEU score of 38.1 for De-En and 42.5 for En-De translation directions on newstest2019. Post-submission, the score is 38.4 for De-En and 42.8 for En-De. Various experiments conducted in the process are also described.

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Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Marco Turchi | Karin Verspoor
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

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Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions
Philipp Koehn | Francisco Guzmán | Vishrav Chaudhary | Juan Pino
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2% and 10% of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task.

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Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings
Vishrav Chaudhary | Yuqing Tang | Francisco Guzmán | Holger Schwenk | Philipp Koehn
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios.

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Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings
Mahsa Yarmohammadi | Xutai Ma | Sorami Hisamoto | Muhammad Rahman | Yiming Wang | Hainan Xu | Daniel Povey | Philipp Koehn | Kevin Duh
Proceedings of Machine Translation Summit XVII: Research Track

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Controlling the Reading Level of Machine Translation Output
Kelly Marchisio | Jialiang Guo | Cheng-I Lai | Philipp Koehn
Proceedings of Machine Translation Summit XVII: Research Track

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Character-Aware Decoder for Translation into Morphologically Rich Languages
Adithya Renduchintala | Pamela Shapiro | Kevin Duh | Philipp Koehn
Proceedings of Machine Translation Summit XVII: Research Track

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De-Mixing Sentiment from Code-Mixed Text
Yash Kumar Lal | Vaibhav Kumar | Mrinal Dhar | Manish Shrivastava | Philipp Koehn
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today’s multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs - the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results - 83.54% accuracy and 0.827 F1 score - on a benchmark dataset.

2018

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Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract)
Rebecca Marvin | Philipp Koehn
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Lightweight Word-Level Confidence Estimation for Neural Interactive Translation Prediction
Rebecca Knowles | Philipp Koehn
Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing

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A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair
Rebecca Knowles | John Ortega | Philipp Koehn
Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing

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Iterative Back-Translation for Neural Machine Translation
Vu Cong Duy Hoang | Philipp Koehn | Gholamreza Haffari | Trevor Cohn
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 German↔English tasks.

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Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation
Huda Khayrallah | Brian Thompson | Kevin Duh | Philipp Koehn
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

Supervised domain adaptation—where a large generic corpus and a smaller in-domain corpus are both available for training—is a challenge for neural machine translation (NMT). Standard practice is to train a generic model and use it to initialize a second model, then continue training the second model on in-domain data to produce an in-domain model. We add an auxiliary term to the training objective during continued training that minimizes the cross entropy between the in-domain model’s output word distribution and that of the out-of-domain model to prevent the model’s output from differing too much from the original out-of-domain model. We perform experiments on EMEA (descriptions of medicines) and TED (rehearsed presentations), initialized from a general domain (WMT) model. Our method shows improvements over standard continued training by up to 1.5 BLEU.

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Document-Level Adaptation for Neural Machine Translation
Sachith Sri Ram Kothur | Rebecca Knowles | Philipp Koehn
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

It is common practice to adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator’s corrections within the document itself. We focus on adaptation within a single document – appropriate for an interactive translation scenario where a model adapts to a human translator’s input over the course of a document. We propose two methods: single-sentence adaptation (which performs online adaptation one sentence at a time) and dictionary adaptation (which specifically addresses the issue of translating novel words). Combining the two models results in improvements over both approaches individually, and over baseline systems, even on short documents. On WMT news test data, we observe an improvement of +1.8 BLEU points and +23.3% novel word translation accuracy and on EMEA data (descriptions of medications) we observe an improvement of +2.7 BLEU points and +49.2% novel word translation accuracy.

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On the Impact of Various Types of Noise on Neural Machine Translation
Huda Khayrallah | Philipp Koehn
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence.

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Proceedings of the Third Conference on Machine Translation: Research Papers
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Lucia Specia | Marco Turchi | Karin Verspoor
Proceedings of the Third Conference on Machine Translation: Research Papers

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Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
Brian Thompson | Huda Khayrallah | Antonios Anastasopoulos | Arya D. McCarthy | Kevin Duh | Rebecca Marvin | Paul McNamee | Jeremy Gwinnup | Tim Anderson | Philipp Koehn
Proceedings of the Third Conference on Machine Translation: Research Papers

To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component’s contribution to, and capacity for, domain adaptation. We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed. We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain.

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Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Lucia Specia | Marco Turchi | Karin Verspoor
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

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Findings of the 2018 Conference on Machine Translation (WMT18)
Ondřej Bojar | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Philipp Koehn | Christof Monz
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018. Participants were asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. This year, we also opened up the task to additional test sets to probe specific aspects of translation.

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The JHU Machine Translation Systems for WMT 2018
Philipp Koehn | Kevin Duh | Brian Thompson
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We report on the efforts of the Johns Hopkins University to develop neural machine translation systems for the shared task for news translation organized around the Conference for Machine Translation (WMT) 2018. We developed systems for German–English, English– German, and Russian–English. Our novel contributions are iterative back-translation and fine-tuning on test sets from prior years.

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Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering
Philipp Koehn | Huda Khayrallah | Kenneth Heafield | Mikel L. Forcada
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We posed the shared task of assigning sentence-level quality scores for a very noisy corpus of sentence pairs crawled from the web, with the goal of sub-selecting 1% and 10% of high-quality data to be used to train machine translation systems. Seventeen participants from companies, national research labs, and universities participated in this task.

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The JHU Parallel Corpus Filtering Systems for WMT 2018
Huda Khayrallah | Hainan Xu | Philipp Koehn
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This work describes our submission to the WMT18 Parallel Corpus Filtering shared task. We use a slightly modified version of the Zipporah Corpus Filtering toolkit (Xu and Koehn, 2017), which computes an adequacy score and a fluency score on a sentence pair, and use a weighted sum of the scores as the selection criteria. This work differs from Zipporah in that we experiment with using the noisy corpus to be filtered to compute the combination weights, and thus avoids generating synthetic data as in standard Zipporah.

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Context and Copying in Neural Machine Translation
Rebecca Knowles | Philipp Koehn
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Neural machine translation systems with subword vocabularies are capable of translating or copying unknown words. In this work, we show that they learn to copy words based on both the context in which the words appear as well as features of the words themselves. In contexts that are particularly copy-prone, they even copy words that they have already learned they should translate. We examine the influence of context and subword features on this and other types of copying behavior.

2017

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Six Challenges for Neural Machine Translation
Philipp Koehn | Rebecca Knowles
Proceedings of the First Workshop on Neural Machine Translation

We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.

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Proceedings of the Second Conference on Machine Translation
Ondřej Bojar | Christian Buck | Rajen Chatterjee | Christian Federmann | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Julia Kreutzer
Proceedings of the Second Conference on Machine Translation

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Predicting Target Language CCG Supertags Improves Neural Machine Translation
Maria Nădejde | Siva Reddy | Rico Sennrich | Tomasz Dwojak | Marcin Junczys-Dowmunt | Philipp Koehn | Alexandra Birch
Proceedings of the Second Conference on Machine Translation

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Findings of the 2017 Conference on Machine Translation (WMT17)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Yvette Graham | Barry Haddow | Shujian Huang | Matthias Huck | Philipp Koehn | Qun Liu | Varvara Logacheva | Christof Monz | Matteo Negri | Matt Post | Raphael Rubino | Lucia Specia | Marco Turchi
Proceedings of the Second Conference on Machine Translation

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The JHU Machine Translation Systems for WMT 2017
Shuoyang Ding | Huda Khayrallah | Philipp Koehn | Matt Post | Gaurav Kumar | Kevin Duh
Proceedings of the Second Conference on Machine Translation

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Knowledge Tracing in Sequential Learning of Inflected Vocabulary
Adithya Renduchintala | Philipp Koehn | Jason Eisner
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

We present a feature-rich knowledge tracing method that captures a student’s acquisition and retention of knowledge during a foreign language phrase learning task. We model the student’s behavior as making predictions under a log-linear model, and adopt a neural gating mechanism to model how the student updates their log-linear parameters in response to feedback. The gating mechanism allows the model to learn complex patterns of retention and acquisition for each feature, while the log-linear parameterization results in an interpretable knowledge state. We collect human data and evaluate several versions of the model.

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Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora
Hainan Xu | Philipp Koehn
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type of bag-of-words translation feature, and train logistic regression models to classify good data and synthetic noisy data in the proposed feature space. The trained model is used to score parallel sentences in the data pool for selection. As shown in experiments, Zipporah selects a high-quality parallel corpus from a large, mixed quality data pool. In particular, for one noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of the data over using the entire corpus.

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Neural Lattice Search for Domain Adaptation in Machine Translation
Huda Khayrallah | Gaurav Kumar | Kevin Duh | Matt Post | Philipp Koehn
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.

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CADET: Computer Assisted Discovery Extraction and Translation
Benjamin Van Durme | Tom Lippincott | Kevin Duh | Deana Burchfield | Adam Poliak | Cash Costello | Tim Finin | Scott Miller | James Mayfield | Philipp Koehn | Craig Harman | Dawn Lawrie | Chandler May | Max Thomas | Annabelle Carrell | Julianne Chaloux | Tongfei Chen | Alex Comerford | Mark Dredze | Benjamin Glass | Shudong Hao | Patrick Martin | Pushpendre Rastogi | Rashmi Sankepally | Travis Wolfe | Ying-Ying Tran | Ted Zhang
Proceedings of the IJCNLP 2017, System Demonstrations

Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.

2016

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User Modeling in Language Learning with Macaronic Texts
Adithya Renduchintala | Rebecca Knowles | Philipp Koehn | Jason Eisner
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Creating Interactive Macaronic Interfaces for Language Learning
Adithya Renduchintala | Rebecca Knowles | Philipp Koehn | Jason Eisner
Proceedings of ACL-2016 System Demonstrations

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Computer Aided Translation
Philipp Koehn
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

Moving beyond post-editing machine translation, a number of recent research efforts have advanced computer aided translation methods that allow for more interactivity, richer information such as confidence scores, and the completed feedback loop of instant adaptation of machine translation models to user translations.This tutorial will explain the main techniques for several aspects of computer aided translation: confidence measures;interactive machine translation (interactive translation prediction);bilingual concordancers;translation option display;paraphrasing (alternative translation suggestions);visualization of word alignment;online adaptation;automatic reviewing;integration of translation memory;eye tracking, logging, and cognitive user models;For each of these, the state of the art and open challenges are presented. The tutorial will also look under the hood of the open source CASMACAT toolkit that is based on MATECAT, and available as a "Home Edition" to be installed on a desktop machine. The target audience of this tutorials are researchers interested in computer aided machine translation and practitioners who want to use or deploy advanced CAT technology.

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Machine Translation Quality and Post-Editor Productivity
Marina Sanchez-Torron | Philipp Koehn
Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track

We assessed how different machine translation (MT) systems affect the post-editing (PE) process and product of professional English–Spanish translators. Our model found that for each 1-point increase in BLEU, there is a PE time decrease of 0.16 seconds per word, about 3-4%. The MT system with the lowest BLEU score produced the output that was post-edited to the lowest quality and with the highest PE effort, measured both in HTER and actual PE operations.

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Neural Interactive Translation Prediction
Rebecca Knowles | Philipp Koehn
Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track

We present an interactive translation prediction method based on neural machine translation. Even with the same translation quality of the underlying machine translation systems, the neural prediction method yields much higher word prediction accuracy (61.6% vs. 43.3%) than the traditional method based on search graphs, mainly due to better recovery from errors. We also develop efficient means to enable practical deployment.

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Translation of Unknown Words in Low Resource Languages
Biman Gujral | Huda Khayrallah | Philipp Koehn
Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track

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Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers
Ondřej Bojar | Christian Buck | Rajen Chatterjee | Christian Federmann | Liane Guillou | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Aurélie Névéol | Mariana Neves | Pavel Pecina | Martin Popel | Philipp Koehn | Christof Monz | Matteo Negri | Matt Post | Lucia Specia | Karin Verspoor | Jörg Tiedemann | Marco Turchi
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation
Maria Nădejde | Alexandra Birch | Philipp Koehn
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers
Ondřej Bojar | Christian Buck | Rajen Chatterjee | Christian Federmann | Liane Guillou | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Aurélie Névéol | Mariana Neves | Pavel Pecina | Martin Popel | Philipp Koehn | Christof Monz | Matteo Negri | Matt Post | Lucia Specia | Karin Verspoor | Jörg Tiedemann | Marco Turchi
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Findings of the 2016 Conference on Machine Translation
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Varvara Logacheva | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Martin Popel | Matt Post | Raphael Rubino | Carolina Scarton | Lucia Specia | Marco Turchi | Karin Verspoor | Marcos Zampieri
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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The JHU Machine Translation Systems for WMT 2016
Shuoyang Ding | Kevin Duh | Huda Khayrallah | Philipp Koehn | Matt Post
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Findings of the WMT 2016 Bilingual Document Alignment Shared Task
Christian Buck | Philipp Koehn
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Quick and Reliable Document Alignment via TF/IDF-weighted Cosine Distance
Christian Buck | Philipp Koehn
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Analyzing Learner Understanding of Novel L2 Vocabulary
Rebecca Knowles | Adithya Renduchintala | Philipp Koehn | Jason Eisner
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

2015

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Findings of the 2015 Workshop on Statistical Machine Translation
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Barry Haddow | Matthias Huck | Chris Hokamp | Philipp Koehn | Varvara Logacheva | Christof Monz | Matteo Negri | Matt Post | Carolina Scarton | Lucia Specia | Marco Turchi
Proceedings of the Tenth Workshop on Statistical Machine Translation

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The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015
Barry Haddow | Matthias Huck | Alexandra Birch | Nikolay Bogoychev | Philipp Koehn
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Edinburgh’s Syntax-Based Systems at WMT 2015
Philip Williams | Rico Sennrich | Maria Nadejde | Matthias Huck | Philipp Koehn
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Results of the WMT15 Metrics Shared Task
Miloš Stanojević | Amir Kamran | Philipp Koehn | Ondřej Bojar
Proceedings of the Tenth Workshop on Statistical Machine Translation

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The Operation Sequence Model—Combining N-Gram-Based and Phrase-Based Statistical Machine Translation
Nadir Durrani | Helmut Schmid | Alexander Fraser | Philipp Koehn | Hinrich Schütze
Computational Linguistics, Volume 41, Issue 2 - June 2015

2014

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Dynamic Topic Adaptation for Phrase-based MT
Eva Hasler | Phil Blunsom | Philipp Koehn | Barry Haddow
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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CASMACAT: A Computer-assisted Translation Workbench
Vicent Alabau | Christian Buck | Michael Carl | Francisco Casacuberta | Mercedes García-Martínez | Ulrich Germann | Jesús González-Rubio | Robin Hill | Philipp Koehn | Luis Leiva | Bartolomé Mesa-Lao | Daniel Ortiz-Martínez | Herve Saint-Amand | Germán Sanchis Trilles | Chara Tsoukala
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Integrating an Unsupervised Transliteration Model into Statistical Machine Translation
Nadir Durrani | Hassan Sajjad | Hieu Hoang | Philipp Koehn
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

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Combining domain and topic adaptation for SMT
Eva Hasler | Barry Haddow | Philipp Koehn
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

Recent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test domains. However, the relationship between domains and latent topics is still somewhat unclear and topic adaptation approaches typically do not make use of domain knowledge in the training data. We show empirically that combining domain and topic adaptation approaches can be beneficial and that topic representations can be used to predict the domain of a test document. Our best combined model yields gains of up to 0.82 BLEU over a domain-adapted translation system and up to 1.67 BLEU over an unadapted system, measured on the stronger of two training conditions.

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Statistical machine translation with the Moses toolkit
Hieu Hoang | Matthias Huck | Philipp Koehn
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: Tutorials

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Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation
Ulrich Germann | Michael Carl | Philipp Koehn | Germán Sanchis-Trilles | Francisco Casacuberta | Robin Hill | Sharon O’Brien
Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation

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The Impact of Machine Translation Quality on Human Post-Editing
Philipp Koehn | Ulrich Germann
Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation

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Using Feature Structures to Improve Verb Translation in English-to-German Statistical MT
Philip Williams | Philipp Koehn
Proceedings of the 3rd Workshop on Hybrid Approaches to Machine Translation (HyTra)

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Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces
Jason Chuang | Spence Green | Marti Hearst | Jeffrey Heer | Philipp Koehn
Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces

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Proceedings of the Ninth Workshop on Statistical Machine Translation
Ondřej Bojar | Christian Buck | Christian Federmann | Barry Haddow | Philipp Koehn | Christof Monz | Matt Post | Lucia Specia
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Findings of the 2014 Workshop on Statistical Machine Translation
Ondřej Bojar | Christian Buck | Christian Federmann | Barry Haddow | Philipp Koehn | Johannes Leveling | Christof Monz | Pavel Pecina | Matt Post | Herve Saint-Amand | Radu Soricut | Lucia Specia | Aleš Tamchyna
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Edinburgh’s Phrase-based Machine Translation Systems for WMT-14
Nadir Durrani | Barry Haddow | Philipp Koehn | Kenneth Heafield
Proceedings of the Ninth Workshop on Statistical Machine Translation

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EU-BRIDGE MT: Combined Machine Translation
Markus Freitag | Stephan Peitz | Joern Wuebker | Hermann Ney | Matthias Huck | Rico Sennrich | Nadir Durrani | Maria Nadejde | Philip Williams | Philipp Koehn | Teresa Herrmann | Eunah Cho | Alex Waibel
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Edinburgh’s Syntax-Based Systems at WMT 2014
Philip Williams | Rico Sennrich | Maria Nadejde | Matthias Huck | Eva Hasler | Philipp Koehn
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Dynamic Topic Adaptation for SMT using Distributional Profiles
Eva Hasler | Barry Haddow | Philipp Koehn
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Augmenting String-to-Tree and Tree-to-String Translation with Non-Syntactic Phrases
Matthias Huck | Hieu Hoang | Philipp Koehn
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Preference Grammars and Soft Syntactic Constraints for GHKM Syntax-based Statistical Machine Translation
Matthias Huck | Hieu Hoang | Philipp Koehn
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Investigating the Usefulness of Generalized Word Representations in SMT
Nadir Durrani | Philipp Koehn | Helmut Schmid | Alexander Fraser
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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The MateCat Tool
Marcello Federico | Nicola Bertoldi | Mauro Cettolo | Matteo Negri | Marco Turchi | Marco Trombetti | Alessandro Cattelan | Antonio Farina | Domenico Lupinetti | Andrea Martines | Alberto Massidda | Holger Schwenk | Loïc Barrault | Frederic Blain | Philipp Koehn | Christian Buck | Ulrich Germann
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

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Edinburgh SLT and MT system description for the IWSLT 2014 evaluation
Alexandra Birch | Matthias Huck | Nadir Durrani | Nikolay Bogoychev | Philipp Koehn
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the University of Edinburgh’s spoken language translation (SLT) and machine translation (MT) systems for the IWSLT 2014 evaluation campaign. In the SLT track, we participated in the German↔English and English→French tasks. In the MT track, we participated in the German↔English, English→French, Arabic↔English, Farsi→English, Hebrew→English, Spanish↔English, and Portuguese-Brazil↔English tasks. For our SLT submissions, we experimented with comparing operation sequence models with bilingual neural network language models. For our MT submissions, we explored using unsupervised transliteration for languages which have a different script than English, in particular for Arabic, Farsi, and Hebrew. We also investigated syntax-based translation and system combination.

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Combined spoken language translation
Markus Freitag | Joern Wuebker | Stephan Peitz | Hermann Ney | Matthias Huck | Alexandra Birch | Nadir Durrani | Philipp Koehn | Mohammed Mediani | Isabel Slawik | Jan Niehues | Eunach Cho | Alex Waibel | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German→English spoken language translation (SLT) track as well as to the German→English, English→German and English→French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system.

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Syntax-Based Statistical Machine Translation
Philip Williams | Philipp Koehn
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

The tutorial explains in detail syntax-based statistical machine translation with synchronous context free grammars (SCFG). It is aimed at researchers who have little background in this area, and gives a comprehensive overview about the main models and methods.While syntax-based models in statistical machine translation have a long history, spanning back almost 20 years, they have only recently shown superior translation quality over the more commonly used phrase-based models, and are now considered state of the art for some language pairs, such as Chinese-English (since ISI's submission to NIST 2006), and English-German (since Edinburgh's submission to WMT 2012).While the field is very dynamic, there is a core set of methods that have become dominant. Such SCFG models are implemented in the open source machine translation toolkit Moses, and the tutors draw from the practical experience of its development.The tutorial focuses on explaining core established concepts in SCFG-based approaches, which are the most popular in this area. The main goal of the tutorial is for the audience to understand how these systems work end-to-end. We review as much relevant literature as necessary, but the tutorial is not a primarily research survey.The tutorial is rounded up with open problems and advanced topics, such as computational challenges, different formalisms for syntax-based models and inclusion of semantics.

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CASMACAT: cognitive analysis and statistical methods for advanced computer aided translation
Philipp Koehn | Michael Carl | Francisco Casacuberta | Eva Marcos
Proceedings of the 17th Annual conference of the European Association for Machine Translation

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Improving machine translation via triangulation and transliteration
Nadir Durrani | Philipp Koehn
Proceedings of the 17th Annual conference of the European Association for Machine Translation

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Refinements to Interactive Translation Prediction Based on Search Graphs
Philipp Koehn | Chara Tsoukala | Herve Saint-Amand
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Grouping Language Model Boundary Words to Speed K–Best Extraction from Hypergraphs
Kenneth Heafield | Philipp Koehn | Alon Lavie
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Proceedings of the Eighth Workshop on Statistical Machine Translation
Ondrej Bojar | Christian Buck | Chris Callison-Burch | Barry Haddow | Philipp Koehn | Christof Monz | Matt Post | Herve Saint-Amand | Radu Soricut | Lucia Specia
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Findings of the 2013 Workshop on Statistical Machine Translation
Ondřej Bojar | Christian Buck | Chris Callison-Burch | Christian Federmann | Barry Haddow | Philipp Koehn | Christof Monz | Matt Post | Radu Soricut | Lucia Specia
Proceedings of the Eighth Workshop on Statistical Machine Translation

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The Feasibility of HMEANT as a Human MT Evaluation Metric
Alexandra Birch | Barry Haddow | Ulrich Germann | Maria Nadejde | Christian Buck | Philipp Koehn
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Edinburgh’s Machine Translation Systems for European Language Pairs
Nadir Durrani | Barry Haddow | Kenneth Heafield | Philipp Koehn
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Edinburgh’s Syntax-Based Machine Translation Systems
Maria Nadejde | Philip Williams | Philipp Koehn
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Abstract Meaning Representation for Sembanking
Laura Banarescu | Claire Bonial | Shu Cai | Madalina Georgescu | Kira Griffitt | Ulf Hermjakob | Kevin Knight | Philipp Koehn | Martha Palmer | Nathan Schneider
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

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English SLT and MT system description for the IWSLT 2013 evaluation
Alexandra Birch | Nadir Durrani | Philipp Koehn
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper gives a description of the University of Edinburgh’s (UEDIN) systems for IWSLT 2013. We participated in all the MT tracks and the German-to-English and Englishto-French SLT tracks. Our SLT submissions experimented with including ASR uncertainty into the decoding process via confusion networks, and looked at different ways of punctuating ASR output. Our MT submissions are mainly based on a system used in the recent evaluation campaign at the Workshop on Statistical Machine Translation [1]. We additionally explored the use of generalized representations (Brown clusters, POS and morphological tags) translating out of English into European languages.

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EU-BRIDGE MT: text translation of talks in the EU-BRIDGE project
Markus Freitag | Stephan Peitz | Joern Wuebker | Hermann Ney | Nadir Durrani | Matthias Huck | Philipp Koehn | Thanh-Le Ha | Jan Niehues | Mohammed Mediani | Teresa Herrmann | Alex Waibel | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

EU-BRIDGE1 is a European research project which is aimed at developing innovative speech translation technology. This paper describes one of the collaborative efforts within EUBRIDGE to further advance the state of the art in machine translation between two European language pairs, English→French and German→English. Four research institutions involved in the EU-BRIDGE project combined their individual machine translation systems and participated with a joint setup in the machine translation track of the evaluation campaign at the 2013 International Workshop on Spoken Language Translation (IWSLT). We present the methods and techniques to achieve high translation quality for text translation of talks which are applied at RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show how we have been able to considerably boost translation performance (as measured in terms of the metrics BLEU and TER) by means of system combination. The joint setups yield empirical gains of up to 1.4 points in BLEU and 2.8 points in TER on the IWSLT test sets compared to the best single systems.

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Dirt Cheap Web-Scale Parallel Text from the Common Crawl
Jason R. Smith | Herve Saint-Amand | Magdalena Plamada | Philipp Koehn | Chris Callison-Burch | Adam Lopez
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning to Prune: Context-Sensitive Pruning for Syntactic MT
Wenduan Xu | Yue Zhang | Philip Williams | Philipp Koehn
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?
Nadir Durrani | Alexander Fraser | Helmut Schmid | Hieu Hoang | Philipp Koehn
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Scalable Modified Kneser-Ney Language Model Estimation
Kenneth Heafield | Ivan Pouzyrevsky | Jonathan H. Clark | Philipp Koehn
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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CASMACAT: Cognitive Analysis and Statistical Methods for Advanced Computer Aided Translation
Philipp Koehn | Michael Carl | Francisco Casacuberta | Eva Marcos
Proceedings of Machine Translation Summit XIV: European projects

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Advanced computer aided translation with a web-based workbench
Vicent Alabau | Ragnar Bonk | Christian Buck | Michael Carl | Francisco Casacuberta | Mercedes García-Martínez | Jesús González | Philipp Koehn | Luis Leiva | Bartolomé Mesa-Lao | Daniel Oriz | Hervé Saint-Amand | Germán Sanchis | Chara Tsiukala
Proceedings of the 2nd Workshop on Post-editing Technology and Practice

2012

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Language Model Rest Costs and Space-Efficient Storage
Kenneth Heafield | Philipp Koehn | Alon Lavie
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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The UEDIN systems for the IWSLT 2012 evaluation
Eva Hasler | Peter Bell | Arnab Ghoshal | Barry Haddow | Philipp Koehn | Fergus McInnes | Steve Renals | Pawel Swietojanski
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the University of Edinburgh (UEDIN) systems for the IWSLT 2012 Evaluation. We participated in the ASR (English), MT (English-French, German-English) and SLT (English-French) tracks.

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Simulating human judgment in machine translation evaluation campaigns
Philipp Koehn
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

We present a Monte Carlo model to simulate human judgments in machine translation evaluation campaigns, such as WMT or IWSLT. We use the model to compare different ranking methods and to give guidance on the number of judgments that need to be collected to obtain sufficiently significant distinctions between systems.

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Sparse lexicalised features and topic adaptation for SMT
Eva Hasler | Barry Haddow | Philipp Koehn
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

We present a new approach to domain adaptation for SMT that enriches standard phrase-based models with lexicalised word and phrase pair features to help the model select appropriate translations for the target domain (TED talks). In addition, we show how source-side sentence-level topics can be incorporated to make the features differentiate between more fine-grained topics within the target domain (topic adaptation). We compare tuning our sparse features on a development set versus on the entire in-domain corpus and introduce a new method of porting them to larger mixed-domain models. Experimental results show that our features improve performance over a MIRA baseline and that in some cases we can get additional improvements with topic features. We evaluate our methods on two language pairs, English-French and German-English, showing promising results.

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Proceedings of the Seventh Workshop on Statistical Machine Translation
Chris Callison-Burch | Philipp Koehn | Christof Monz | Matt Post | Radu Soricut | Lucia Specia
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Findings of the 2012 Workshop on Statistical Machine Translation
Chris Callison-Burch | Philipp Koehn | Christof Monz | Matt Post | Radu Soricut | Lucia Specia
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Towards Effective Use of Training Data in Statistical Machine Translation
Philipp Koehn | Barry Haddow
Proceedings of the Seventh Workshop on Statistical Machine Translation

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GHKM Rule Extraction and Scope-3 Parsing in Moses
Philip Williams | Philipp Koehn
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Analysing the Effect of Out-of-Domain Data on SMT Systems
Barry Haddow | Philipp Koehn
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Interpolated Backoff for Factored Translation Models
Philipp Koehn | Barry Haddow
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

We propose interpolated backoff methods to strike the balance between traditional surface form translation models and factored models that decompose translation into lemma and morphological feature mapping steps. We show that this approach improves translation quality by 0.5 BLEU (German–English) over phrase-based models, due to the better translation of rare nouns and adjectives.

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Open Source Statistical Machine Translation
Philipp Koehn | Hieu Hoang
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Tutorials

If you are interested in open-source machine translation but lack hands-on experience, this is the tutorial for you! We will start with background knowledge of statistical machine translation and then walk you through the process of installing and running an SMT system. We will show you how to prepare input data, and the most efficient way to train and use your translation systems. We shall also discuss solutions to some of the most common issues that face LSPs when using SMT, including how to tailor systems to specific clients, preserving document layout and formatting, and efficient ways of incorporating new translation memories. Previous years’ participants have included software engineers and managers who need to have a detailed understanding of the SMT process. This is a fast-paced, hands-on tutorial that will cover the skills you need to get you up and running with open-source SMT. The teaching will be based on the Moses toolkit, the most popular open-source machine translation software currently available. No prior knowledge of MT is necessary, only an interest in it. A laptop is required for this tutorial, and you should have rudimentary knowledge of using the command line on Windows or Linux.

2011

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Proceedings of the Sixth Workshop on Statistical Machine Translation
Chris Callison-Burch | Philipp Koehn | Christof Monz | Omar F. Zaidan
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Findings of the 2011 Workshop on Statistical Machine Translation
Chris Callison-Burch | Philipp Koehn | Christof Monz | Omar Zaidan
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Agreement Constraints for Statistical Machine Translation into German
Philip Williams | Philipp Koehn
Proceedings of the Sixth Workshop on Statistical Machine Translation

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SampleRank Training for Phrase-Based Machine Translation
Barry Haddow | Abhishek Arun | Philipp Koehn
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Soft Dependency Constraints for Reordering in Hierarchical Phrase-Based Translation
Yang Gao | Philipp Koehn | Alexandra Birch
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Left language model state for syntactic machine translation
Kenneth Heafield | Hieu Hoang | Philipp Koehn | Tetsuo Kiso | Marcello Federico
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

Many syntactic machine translation decoders, including Moses, cdec, and Joshua, implement bottom-up dynamic programming to integrate N-gram language model probabilities into hypothesis scoring. These decoders concatenate hypotheses according to grammar rules, yielding larger hypotheses and eventually complete translations. When hypotheses are concatenated, the language model score is adjusted to account for boundary-crossing n-grams. Words on the boundary of each hypothesis are encoded in state, consisting of left state (the first few words) and right state (the last few words). We speed concatenation by encoding left state using data structure pointers in lieu of vocabulary indices and by avoiding unnecessary queries. To increase the decoder’s opportunities to recombine hypothesis, we minimize the number of words encoded by left state. This has the effect of reducing search errors made by the decoder. The resulting gain in model score is smaller than for right state minimization, which we explain by observing a relationship between state minimization and language model probability. With a fixed cube pruning pop limit, we show a 3-6% reduction in CPU time and improved model scores. Reducing the pop limit to the point where model scores tie the baseline yields a net 11% reduction in CPU time.

2010

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Convergence of Translation Memory and Statistical Machine Translation
Philipp Koehn | Jean Senellart
Proceedings of the Second Joint EM+/CNGL Workshop: Bringing MT to the User: Research on Integrating MT in the Translation Industry

We present two methods that merge ideas from statistical machine translation (SMT) and translation memories (TM). We use a TM to retrieve matches for source segments, and replace the mismatched parts with instructions to an SMT system to fill in the gap. We show that for fuzzy matches of over 70%, one method outperforms both SMT and TM baselines.

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Enabling Monolingual Translators: Post-Editing vs. Options
Philipp Koehn
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Chris Callison-Burch | Philipp Koehn | Christof Monz | Kay Peterson | Omar Zaidan
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation
Chris Callison-Burch | Philipp Koehn | Christof Monz | Kay Peterson | Mark Przybocki | Omar Zaidan
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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More Linguistic Annotation for Statistical Machine Translation
Philipp Koehn | Barry Haddow | Philip Williams | Hieu Hoang
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Aiding Pronoun Translation with Co-Reference Resolution
Ronan Le Nagard | Philipp Koehn
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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A Unified Approach to Minimum Risk Training and Decoding
Abhishek Arun | Barry Haddow | Philipp Koehn
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Improved Translation with Source Syntax Labels
Hieu Hoang | Philipp Koehn
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Fast Approximate String Matching with Suffix Arrays and A* Parsing
Philipp Koehn | Jean Senellart
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

We present a novel exact solution to the approximate string matching problem in the context of translation memories, where a text segment has to be matched against a large corpus, while allowing for errors. We use suffix arrays to detect exact n-gram matches, A* search heuristics to discard matches and A* parsing to validate candidate segments. The method outperforms the canonical baseline by a factor of 100, with average lookup times of 4.3–247ms for a segment in a realistic scenario.

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Machine Translation with Open source Software
Philipp Koehn | Hieu Hoang
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Tutorials

2009

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462 Machine Translation Systems for Europe
Philipp Koehn | Alexandra Birch | Ralf Steinberger
Proceedings of Machine Translation Summit XII: Papers

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Interactive Assistance to Human Translators using Statistical Machine Translation Methods
Philipp Koehn | Barry Haddow
Proceedings of Machine Translation Summit XII: Papers

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Selective addition of corpus-extracted phrasal lexical rules to a rule-based machine translation system
Loic Dugast | Jean Senellart | Philipp Koehn
Proceedings of Machine Translation Summit XII: Posters

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Panel on Patent Translation
Philipp Koehn
Proceedings of the Third Workshop on Patent Translation

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A Web-Based Interactive Computer Aided Translation Tool
Philipp Koehn
Proceedings of the ACL-IJCNLP 2009 Software Demonstrations

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Topics in Statistical Machine Translation
Kevin Knight | Philipp Koehn
Tutorial Abstracts of ACL-IJCNLP 2009

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Improving Mid-Range Re-Ordering Using Templates of Factors
Hieu Hoang | Philipp Koehn
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Word Lattices for Multi-Source Translation
Josh Schroeder | Trevor Cohn | Philipp Koehn
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing
Philipp Koehn | Rada Mihalcea
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Human translation and machine translation
Philipp Koehn
Proceedings of the 6th International Workshop on Spoken Language Translation: Plenaries

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A unified framework for phrase-based, hierarchical, and syntax-based statistical machine translation
Hieu Hoang | Philipp Koehn | Adam Lopez
Proceedings of the 6th International Workshop on Spoken Language Translation: Papers

Despite many differences between phrase-based, hierarchical, and syntax-based translation models, their training and testing pipelines are strikingly similar. Drawing on this fact, we extend the Moses toolkit to implement hierarchical and syntactic models, making it the first open source toolkit with end-to-end support for all three of these popular models in a single package. This extension substantially lowers the barrier to entry for machine translation research across multiple models.

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Proceedings of the Fourth Workshop on Statistical Machine Translation
Chris Callison-Burch | Philipp Koehn | Christof Monz | Josh Schroeder
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Findings of the 2009 Workshop on Statistical Machine Translation
Chris Callison-Burch | Philipp Koehn | Christof Monz | Josh Schroeder
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Statistical Post Editing and Dictionary Extraction: Systran/Edinburgh Submissions for ACL-WMT2009
Loic Dugast | Jean Senellart | Philipp Koehn
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Edinburgh’s Submission to all Tracks of the WMT 2009 Shared Task with Reordering and Speed Improvements to Moses
Philipp Koehn | Barry Haddow
Proceedings of the Fourth Workshop on Statistical Machine Translation

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A Systematic Analysis of Translation Model Search Spaces
Michael Auli | Adam Lopez | Hieu Hoang | Philipp Koehn
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Monte Carlo inference and maximization for phrase-based translation
Abhishek Arun | Chris Dyer | Barry Haddow | Phil Blunsom | Adam Lopez | Philipp Koehn
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

2008

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Proceedings of the Third Workshop on Statistical Machine Translation
Chris Callison-Burch | Philipp Koehn | Christof Monz | Josh Schroeder | Cameron Shaw Fordyce
Proceedings of the Third Workshop on Statistical Machine Translation

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Further Meta-Evaluation of Machine Translation
Chris Callison-Burch | Cameron Fordyce | Philipp Koehn | Christof Monz | Josh Schroeder
Proceedings of the Third Workshop on Statistical Machine Translation

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Towards better Machine Translation Quality for the German-English Language Pairs
Philipp Koehn | Abhishek Arun | Hieu Hoang
Proceedings of the Third Workshop on Statistical Machine Translation

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Can we Relearn an RBMT System?
Loïc Dugast | Jean Senellart | Philipp Koehn
Proceedings of the Third Workshop on Statistical Machine Translation

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Design of the Moses Decoder for Statistical Machine Translation
Hieu Hoang | Philipp Koehn
Software Engineering, Testing, and Quality Assurance for Natural Language Processing

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Predicting Success in Machine Translation
Alexandra Birch | Miles Osborne | Philipp Koehn
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Large and Diverse Language Models for Statistical Machine Translation
Holger Schwenk | Philipp Koehn
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

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Enriching Morphologically Poor Languages for Statistical Machine Translation
Eleftherios Avramidis | Philipp Koehn
Proceedings of ACL-08: HLT

2007

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Proceedings of the Second Workshop on Statistical Machine Translation
Chris Callison-Burch | Philipp Koehn | Cameron Shaw Fordyce | Christof Monz
Proceedings of the Second Workshop on Statistical Machine Translation

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CCG Supertags in Factored Statistical Machine Translation
Alexandra Birch | Miles Osborne | Philipp Koehn
Proceedings of the Second Workshop on Statistical Machine Translation

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(Meta-) Evaluation of Machine Translation
Chris Callison-Burch | Cameron Fordyce | Philipp Koehn | Christof Monz | Josh Schroeder
Proceedings of the Second Workshop on Statistical Machine Translation

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Statistical Post-Editing on SYSTRAN‘s Rule-Based Translation System
Loïc Dugast | Jean Senellart | Philipp Koehn
Proceedings of the Second Workshop on Statistical Machine Translation

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Experiments in Domain Adaptation for Statistical Machine Translation
Philipp Koehn | Josh Schroeder
Proceedings of the Second Workshop on Statistical Machine Translation

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Chinese Syntactic Reordering for Statistical Machine Translation
Chao Wang | Michael Collins | Philipp Koehn
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Factored Translation Models
Philipp Koehn | Hieu Hoang
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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EuroMatrix – machine translation for all European languages
Philipp Koehn
Proceedings of Machine Translation Summit XI: Invited papers

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Online learning methods for discriminative training of phrase based statistical machine translation
Abhishek Arun | Philipp Koehn
Proceedings of Machine Translation Summit XI: Papers

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Statistical machine translation
Kevin Knight | Philipp Koehn
Proceedings of Machine Translation Summit XI: Tutorials

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Evaluating evaluation – lessons from the WMT 2007 shared task
Philipp Koehn | Chris Callison-Burch
Proceedings of the Workshop on Automatic procedures in MT evaluation

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Moses: Open Source Toolkit for Statistical Machine Translation
Philipp Koehn | Hieu Hoang | Alexandra Birch | Chris Callison-Burch | Marcello Federico | Nicola Bertoldi | Brooke Cowan | Wade Shen | Christine Moran | Richard Zens | Chris Dyer | Ondřej Bojar | Alexandra Constantin | Evan Herbst
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

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The University of Edinburgh system description for IWSLT 2007
Josh Schroeder | Philipp Koehn
Proceedings of the Fourth International Workshop on Spoken Language Translation

We present the University of Edinburgh’s submission for the IWSLT 2007 shared task. Our efforts focused on adapting our statistical machine translation system to the open data conditions for the Italian-English task of the evaluation campaign. We examine the challenges of building a system with a limited set of in-domain development data (SITAL), a small training corpus in a related but distinct domain (BTEC), and a large out of domain corpus (Europarl). We concentrated on the corrected text track, and present additional results of our experiments using the open-source Moses MT system with speech input.

2006

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Proceedings on the Workshop on Statistical Machine Translation
Philipp Koehn | Christof Monz
Proceedings on the Workshop on Statistical Machine Translation

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Manual and Automatic Evaluation of Machine Translation between European Languages
Philipp Koehn | Christof Monz
Proceedings on the Workshop on Statistical Machine Translation

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Constraining the Phrase-Based, Joint Probability Statistical Translation Model
Alexandra Birch | Chris Callison-Burch | Miles Osborne | Philipp Koehn
Proceedings on the Workshop on Statistical Machine Translation

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Statistical machine translation and hybrid machine translation
Philipp Koehn
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Panel on hybrid machine translation: why and how?

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Improved Statistical Machine Translation Using Paraphrases
Chris Callison-Burch | Philipp Koehn | Miles Osborne
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

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Re-evaluating the Role of Bleu in Machine Translation Research
Chris Callison-Burch | Miles Osborne | Philipp Koehn
11th Conference of the European Chapter of the Association for Computational Linguistics

2005

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Edinburgh System Description for the 2005 IWSLT Speech Translation Evaluation
Philipp Koehn | Amittai Axelrod | Alexandra Birch Mayne | Chris Callison-Burch | Miles Osborne | David Talbot
Proceedings of the Second International Workshop on Spoken Language Translation

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Europarl: A Parallel Corpus for Statistical Machine Translation
Philipp Koehn
Proceedings of Machine Translation Summit X: Papers

We collected a corpus of parallel text in 11 languages from the proceedings of the European Parliament, which are published on the web. This corpus has found widespread use in the NLP community. Here, we focus on its acquisition and its application as training data for statistical machine translation (SMT). We trained SMT systems for 110 language pairs, which reveal interesting clues into the challenges ahead.

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Proceedings of the ACL Workshop on Building and Using Parallel Texts
Philipp Koehn | Joel Martin | Rada Mihalcea | Christof Monz | Ted Pedersen
Proceedings of the ACL Workshop on Building and Using Parallel Texts

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Shared Task: Statistical Machine Translation between European Languages
Philipp Koehn | Christof Monz
Proceedings of the ACL Workshop on Building and Using Parallel Texts

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Clause Restructuring for Statistical Machine Translation
Michael Collins | Philipp Koehn | Ivona Kučerová
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2004

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Statistical Significance Tests for Machine Translation Evaluation
Philipp Koehn
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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Introduction to statistical machine translation
Philipp Koehn | Kevin Knight
Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Tutorial Descriptions

2003

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Statistical Phrase-Based Translation
Philipp Koehn | Franz J. Och | Daniel Marcu
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

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Desparately Seeking Cebuano
Douglas W. Oard | David Doermann | Bonnie Dorr | Daqing He | Philip Resnik | Amy Weinberg | William Byrne | Sanjeev Khudanpur | David Yarowsky | Anton Leuski | Philipp Koehn | Kevin Knight
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

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What’s New in Statistical Machine Translation
Kevin Knight | Philipp Koehn
Companion Volume of the Proceedings of HLT-NAACL 2003 - Tutorial Abstracts

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Feature-Rich Statistical Translation of Noun Phrases
Philipp Koehn | Kevin Knight
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Empirical Methods for Compound Splitting
Philipp Koehn | Kevin Knight
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Learning a Translation Lexicon from Monolingual Corpora
Philipp Koehn | Kevin Knight
Proceedings of the ACL-02 Workshop on Unsupervised Lexical Acquisition

2001

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Knowledge Sources for Word-Level Translation Models
Philipp Koehn | Kevin Knight
Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing

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