Matt Post


2021

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Levenshtein Training for Word-level Quality Estimation
Shuoyang Ding | Marcin Junczys-Dowmunt | Matt Post | Philipp Koehn
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We propose a novel scheme to use the Levenshtein Transformer to perform the task of word-level quality estimation. A Levenshtein Transformer is a natural fit for this task: trained to perform decoding in an iterative manner, a Levenshtein Transformer can learn to post-edit without explicit supervision. To further minimize the mismatch between the translation task and the word-level QE task, we propose a two-stage transfer learning procedure on both augmented data and human post-editing data. We also propose heuristics to construct reference labels that are compatible with subword-level finetuning and inference. Results on WMT 2020 QE shared task dataset show that our proposed method has superior data efficiency under the data-constrained setting and competitive performance under the unconstrained setting.

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Robust Open-Vocabulary Translation from Visual Text Representations
Elizabeth Salesky | David Etter | Matt Post
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Machine translation models have discrete vocabularies and commonly use subword segmentation techniques to achieve an ‘open vocabulary.’ This approach relies on consistent and correct underlying unicode sequences, and makes models susceptible to degradation from common types of noise and variation. Motivated by the robustness of human language processing, we propose the use of visual text representations, which dispense with a finite set of text embeddings in favor of continuous vocabularies created by processing visually rendered text with sliding windows. We show that models using visual text representations approach or match performance of traditional text models on small and larger datasets. More importantly, models with visual embeddings demonstrate significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a character permuted German–English task where subword models degrade to 1.9.

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A unified approach to sentence segmentation of punctuated text in many languages
Rachel Wicks | Matt Post
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 sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines is sentence segmentation. Despite its importance, this step is the subject of relatively little research. There are no standard test sets or even methods for evaluation, leaving researchers and engineers without a clear footing for evaluating and selecting models for the task. Existing tools have relatively small language coverage, and efforts to extend them to other languages are often ad hoc. We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. We also establish a new 23-language multilingual evaluation set. Our approach exceeds high baselines set by existing methods on prior English corpora (WSJ and Brown corpora), and also performs well on average on our new evaluation set. We release our tool, ersatz, as open source.

2020

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On the Evaluation of Machine Translation n-best Lists
Jacob Bremerman | Huda Khayrallah | Douglas Oard | Matt Post
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems

The standard machine translation evaluation framework measures the single-best output of machine translation systems. There are, however, many situations where n-best lists are needed, yet there is no established way of evaluating them. This paper establishes a framework for addressing n-best evaluation by outlining three different questions one could consider when determining how one would define a ‘good’ n-best list and proposing evaluation measures for each question. The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list. The second is a measure that uses gold translations with preference annotations to ask to what degree systems can produce ranked lists in preference order. The third is a measure that rewards partial matches, evaluating the closeness of the many items in an n-best list to a set of many valid references. These three perspectives make clear that having access to many references can be useful when n-best evaluation is the goal.

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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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Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing
Brian Thompson | Matt Post
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech). This results in the paraphraser’s output mode being centered around a copy of the input sequence, which represents the best case scenario where the MT system output matches a human reference. Our method is simple and intuitive, and does not require human judgements for training. Our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages (excluding Gujarati where the model had no training data). We also explore using our model for the task of quality estimation as a metric—conditioning on the source instead of the reference—and find that it significantly outperforms every submission to the WMT 2019 shared task on quality estimation in every language pair.

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Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?
Sorami Hisamoto | Matt Post | Kevin Duh
Transactions of the Association for Computational Linguistics, Volume 8

Data privacy is an important issue for “machine learning as a service” providers. We focus on the problem of membership inference attacks: Given a data sample and black-box access to a model’s API, determine whether the sample existed in the model’s training data. Our contribution is an investigation of this problem in the context of sequence-to-sequence models, which are important in applications such as machine translation and video captioning. We define the membership inference problem for sequence generation, provide an open dataset based on state-of-the-art machine translation models, and report initial results on whether these models leak private information against several kinds of membership inference attacks.

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The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education
Huda Khayrallah | Jacob Bremerman | Arya D. McCarthy | Kenton Murray | Winston Wu | Matt Post
Proceedings of the Fourth Workshop on Neural Generation and Translation

This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE). We participated in all five language tasks, placing first in each. Our approach involved a language-agnostic pipeline of three components: (1) building strong machine translation systems on general-domain data, (2) fine-tuning on Duolingo-provided data, and (3) generating n-best lists which are then filtered with various score-based techniques. In addi- tion to the language-agnostic pipeline, we attempted a number of linguistically-motivated approaches, with, unfortunately, little success. We also find that improving BLEU performance of the beam-search generated translation does not necessarily improve on the task metric—weighted macro F1 of an n-best list.

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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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Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity
Brian Thompson | Matt Post
Proceedings of the Fifth Conference on Machine Translation

Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.

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ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task
Rachel Bawden | Biao Zhang | Andre Tättar | Matt Post
Proceedings of the Fifth Conference on Machine Translation

We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references.

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Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages
Kevin Duh | Paul McNamee | Matt Post | Brian Thompson
Proceedings of the 12th Language Resources and Evaluation Conference

Research in machine translation (MT) is developing at a rapid pace. However, most work in the community has focused on languages where large amounts of digital resources are available. In this study, we benchmark state of the art statistical and neural machine translation systems on two African languages which do not have large amounts of resources: Somali and Swahili. These languages are of social importance and serve as test-beds for developing technologies that perform reasonably well despite the low-resource constraint. Our findings suggest that statistical machine translation (SMT) and neural machine translation (NMT) can perform similarly in low-resource scenarios, but neural systems require more careful tuning to match performance. We also investigate how to exploit additional data, such as bilingual text harvested from the web, or user dictionaries; we find that NMT can significantly improve in performance with the use of these additional data. Finally, we survey the landscape of machine translation resources for the languages of Africa and provide some suggestions for promising future research directions.

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The Johns Hopkins University Bible Corpus: 1600+ Tongues for Typological Exploration
Arya D. McCarthy | Rachel Wicks | Dylan Lewis | Aaron Mueller | Winston Wu | Oliver Adams | Garrett Nicolai | Matt Post | David Yarowsky
Proceedings of the 12th Language Resources and Evaluation Conference

We present findings from the creation of a massively parallel corpus in over 1600 languages, the Johns Hopkins University Bible Corpus (JHUBC). The corpus consists of over 4000 unique translations of the Christian Bible and counting. Our data is derived from scraping several online resources and merging them with existing corpora, combining them under a common scheme that is verse-parallel across all translations. We detail our effort to scrape, clean, align, and utilize this ripe multilingual dataset. The corpus captures the great typological variety of the world’s languages. We catalog this by showing highly similar proportions of representation of Ethnologue’s typological features in our corpus. We also give an example application: projecting pronoun features like clusivity across alignments to richly annotate languages which do not mark the distinction.

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A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing
Rachel Bawden | Biao Zhang | Lisa Yankovskaya | Andre Tättar | Matt Post
Findings of the Association for Computational Linguistics: EMNLP 2020

We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques, we study whether automatically generating additional *diverse* references can provide better coverage of the space of valid translations and thereby improve its correlation with human judgments. Our experiments on the into-English language directions of the WMT19 metrics task (at both the system and sentence level) show that using paraphrased references does generally improve BLEU, and when it does, the more diverse the better. However, we also show that better results could be achieved if those paraphrases were to specifically target the parts of the space most relevant to the MT outputs being evaluated. Moreover, the gains remain slight even when human paraphrases are used, suggesting inherent limitations to BLEU’s capacity to correctly exploit multiple references. Surprisingly, we also find that adequacy appears to be less important, as shown by the high results of a strong sampling approach, which even beats human paraphrases when used with sentence-level BLEU.

2019

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A Discriminative Neural Model for Cross-Lingual Word Alignment
Elias Stengel-Eskin | Tzu-ray Su | Matt Post | Benjamin Van Durme
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 a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples (∼1.7K–5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11–27 F1). We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese. Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation.

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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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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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JHU 2019 Robustness Task System Description
Matt Post | Kevin Duh
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

We describe the JHU submissions to the French–English, Japanese–English, and English–Japanese Robustness Task at WMT 2019. Our goal was to evaluate the performance of baseline systems on both the official noisy test set as well as news data, in order to ensure that performance gains in the latter did not come at the expense of general-domain performance. To this end, we built straightforward 6-layer Transformer models and experimented with a handful of variables including subword processing (FR→EN) and a handful of hyperparameters settings (JA↔EN). As expected, our systems performed reasonably.

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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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An Exploration of Placeholding in Neural Machine Translation
Matt Post | Shuoyang Ding | Marianna Martindale | Winston Wu
Proceedings of Machine Translation Summit XVII: Research Track

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Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting
J. Edward Hu | Huda Khayrallah | Ryan Culkin | Patrick Xia | Tongfei Chen | Matt Post | Benjamin Van Durme
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)

Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decoding to work with batching, leading to a five-fold improvement in throughput when working with positive constraints. Faster decoding enables faster exploration of constraint strategies: we illustrate this via data augmentation experiments with a monolingual rewriter applied to the tasks of natural language inference, question answering and machine translation, showing improvements in all three.

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Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering
J. Edward Hu | Abhinav Singh | Nils Holzenberger | Matt Post | Benjamin Van Durme
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Producing diverse paraphrases of a sentence is a challenging task. Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam search, which tends to lack diversity. We describe ParaBank 2, a new resource that contains multiple diverse sentential paraphrases, produced from a bilingual corpus using negative constraints, inference sampling, and clustering.We show that ParaBank 2 significantly surpasses prior work in both lexical and syntactic diversity while being meaning-preserving, as measured by human judgments and standardized metrics. Further, we illustrate how such paraphrastic resources may be used to refine contextualized encoders, leading to improvements in downstream tasks.

2018

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Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation
Matt Post | David Vilar
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however, has introduced a new capability: lexically constrained or guided decoding, a modification to beam search that forces the inclusion of pre-specified words and phrases in the output. However, while theoretically sound, existing approaches have computational complexities that are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017) in the number of constraints. We present a algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. We demonstrate the algorithm’s remarkable ability to properly place these constraints, and use it to explore the shaky relationship between model and BLEU scores. Our implementation is available as part of Sockeye.

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The Sockeye Neural Machine Translation Toolkit at AMTA 2018
Felix Hieber | Tobias Domhan | Michael Denkowski | David Vilar | Artem Sokolov | Ann Clifton | Matt Post
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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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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A Call for Clarity in Reporting BLEU Scores
Matt Post
Proceedings of the Third Conference on Machine Translation: Research Papers

The field of machine translation faces an under-recognized problem because of inconsistency in the reporting of scores from its dominant metric. Although people refer to “the” BLEU score, BLEU is in fact a parameterized metric whose values can vary wildly with changes to these parameters. These parameters are often not reported or are hard to find, and consequently, BLEU scores between papers cannot be directly compared. I quantify this variation, finding differences as high as 1.8 between commonly used configurations. The main culprit is different tokenization and normalization schemes applied to the reference. Pointing to the success of the parsing community, I suggest machine translation researchers settle upon the BLEU scheme used by the annual Conference on Machine Translation (WMT), which does not allow for user-supplied reference processing, and provide a new tool, SACREBLEU, to facilitate this.

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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

2017

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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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Grammatical Error Correction with Neural Reinforcement Learning
Keisuke Sakaguchi | Matt Post | Benjamin Van Durme
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus.

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A Rich Morphological Tagger for English: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax
Christo Kirov | John Sylak-Glassman | Rebecca Knowles | Ryan Cotterell | Matt Post
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

A traditional claim in linguistics is that all human languages are equally expressive—able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. Languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation.

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Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Lucia Specia | Matt Post | Michael Paul
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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Error-repair Dependency Parsing for Ungrammatical Texts
Keisuke Sakaguchi | Matt Post | Benjamin Van Durme
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transition-based formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme.

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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

2016

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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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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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Sentential Paraphrasing as Black-Box Machine Translation
Courtney Napoles | Chris Callison-Burch | Matt Post
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality
Keisuke Sakaguchi | Courtney Napoles | Matt Post | Joel Tetreault
Transactions of the Association for Computational Linguistics, Volume 4

The field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics. One unvisited assumption, however, is the reliance of GEC evaluation on error-coded corpora, which contain specific labeled corrections. We examine current practices and show that GEC’s reliance on such corpora unnaturally constrains annotation and automatic evaluation, resulting in (a) sentences that do not sound acceptable to native speakers and (b) system rankings that do not correlate with human judgments. In light of this, we propose an alternate approach that jettisons costly error coding in favor of unannotated, whole-sentence rewrites. We compare the performance of existing metrics over different gold-standard annotations, and show that automatic evaluation with our new annotation scheme has very strong correlation with expert rankings (ρ = 0.82). As a result, we advocate for a fundamental and necessary shift in the goal of GEC, from correcting small, labeled error types, to producing text that has native fluency.

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Automatic Construction of Morphologically Motivated Translation Models for Highly Inflected, Low-Resource Languages
John Hewitt | Matt Post | David Yarowsky
Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track

Statistical Machine Translation (SMT) of highly inflected, low-resource languages suffers from the problem of low bitext availability, which is exacerbated by large inflectional paradigms. When translating into English, rich source inflections have a high chance of being poorly estimated or out-of-vocabulary (OOV). We present a source language-agnostic system for automatically constructing phrase pairs from foreign-language inflections and their morphological analyses using manually constructed datasets, including Wiktionary. We then demonstrate the utility of these phrase tables in improving translation into English from Finnish, Czech, and Turkish in simulated low-resource settings, finding substantial gains in translation quality. We report up to +2.58 BLEU in a simulated low-resource setting and +1.65 BLEU in a moderateresource setting. We release our morphologically-motivated translation models, with tens of thousands of inflections in each of 8 languages.

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Putting the “human” back in HLT: The importance of human evaluation in assessing the quality and potential uses of translation technology
Erica Michael | Petra Bradley | Paul McNamee | Matt Post
Conferences of the Association for Machine Translation in the Americas: MT Users' Track

2015

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Ground Truth for Grammatical Error Correction Metrics
Courtney Napoles | Keisuke Sakaguchi | Matt Post | Joel Tetreault
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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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

2014

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The Language Demographics of Amazon Mechanical Turk
Ellie Pavlick | Matt Post | Ann Irvine | Dmitry Kachaev | Chris Callison-Burch
Transactions of the Association for Computational Linguistics, Volume 2

We present a large scale study of the languages spoken by bilingual workers on Mechanical Turk (MTurk). We establish a methodology for determining the language skills of anonymous crowd workers that is more robust than simple surveying. We validate workers’ self-reported language skill claims by measuring their ability to correctly translate words, and by geolocating workers to see if they reside in countries where the languages are likely to be spoken. Rather than posting a one-off survey, we posted paid tasks consisting of 1,000 assignments to translate a total of 10,000 words in each of 100 languages. Our study ran for several months, and was highly visible on the MTurk crowdsourcing platform, increasing the chances that bilingual workers would complete it. Our study was useful both to create bilingual dictionaries and to act as census of the bilingual speakers on MTurk. We use this data to recommend languages with the largest speaker populations as good candidates for other researchers who want to develop crowdsourced, multilingual technologies. To further demonstrate the value of creating data via crowdsourcing, we hire workers to create bilingual parallel corpora in six Indian languages, and use them to train statistical machine translation systems.

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A Wikipedia-based Corpus for Contextualized Machine Translation
Jennifer Drexler | Pushpendre Rastogi | Jacqueline Aguilar | Benjamin Van Durme | Matt Post
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.

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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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Efficient Elicitation of Annotations for Human Evaluation of Machine Translation
Keisuke Sakaguchi | Matt Post | Benjamin Van Durme
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

2013

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Explicit and Implicit Syntactic Features for Text Classification
Matt Post | Shane Bergsma
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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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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Joshua 5.0: Sparser, Better, Faster, Server
Matt Post | Juri Ganitkevitch | Luke Orland | Jonathan Weese | Yuan Cao | Chris Callison-Burch
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Improved speech-to-text translation with the Fisher and Callhome Spanish-English speech translation corpus
Matt Post | Gaurav Kumar | Adam Lopez | Damianos Karakos | Chris Callison-Burch | Sanjeev Khudanpur
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

Research into the translation of the output of automatic speech recognition (ASR) systems is hindered by the dearth of datasets developed for that explicit purpose. For SpanishEnglish translation, in particular, most parallel data available exists only in vastly different domains and registers. In order to support research on cross-lingual speech applications, we introduce the Fisher and Callhome Spanish-English Speech Translation Corpus, supplementing existing LDC audio and transcripts with (a) ASR 1-best, lattice, and oracle output produced by the Kaldi recognition system and (b) English translations obtained on Amazon’s Mechanical Turk. The result is a four-way parallel dataset of Spanish audio, transcriptions, ASR lattices, and English translations of approximately 38 hours of speech, with defined training, development, and held-out test sets. We conduct baseline machine translation experiments using models trained on the provided training data, and validate the dataset by corroborating a number of known results in the field, including the utility of in-domain (information, conversational) training data, increased performance translating lattices (instead of recognizer 1-best output), and the relationship between word error rate and BLEU score.

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Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT
Adam Lopez | Matt Post | Chris Callison-Burch | Jonathan Weese | Juri Ganitkevitch | Narges Ahmidi | Olivia Buzek | Leah Hanson | Beenish Jamil | Matthias Lee | Ya-Ting Lin | Henry Pao | Fatima Rivera | Leili Shahriyari | Debu Sinha | Adam Teichert | Stephen Wampler | Michael Weinberger | Daguang Xu | Lin Yang | Shang Zhao
Transactions of the Association for Computational Linguistics, Volume 1

Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve performance on carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and reranking. Students brought a diverse set of techniques to the problems, including some novel solutions which performed remarkably well. A surprising and exciting outcome was that student solutions or their combinations fared competitively on some tasks, demonstrating that even newcomers to the field can help improve the state-of-the-art on hard NLP problems while simultaneously learning a great deal. The problems, baseline code, and results are freely available.

2012

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Toward Tree Substitution Grammars with Latent Annotations
Francis Ferraro | Benjamin Van Durme | Matt Post
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure

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Judging Grammaticality with Count-Induced Tree Substitution Grammars
Francis Ferraro | Matt Post | Benjamin Van Durme
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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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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Joshua 4.0: Packing, PRO, and Paraphrases
Juri Ganitkevitch | Yuan Cao | Jonathan Weese | Matt Post | Chris Callison-Burch
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Constructing Parallel Corpora for Six Indian Languages via Crowdsourcing
Matt Post | Chris Callison-Burch | Miles Osborne
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Stylometric Analysis of Scientific Articles
Shane Bergsma | Matt Post | David Yarowsky
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Judging Grammaticality with Tree Substitution Grammar Derivations
Matt Post
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Joshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor
Jonathan Weese | Juri Ganitkevitch | Chris Callison-Burch | Matt Post | Adam Lopez
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

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Factors Affecting the Accuracy of Korean Parsing
Tagyoung Chung | Matt Post | Daniel Gildea
Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages

2009

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Bayesian Learning of a Tree Substitution Grammar
Matt Post | Daniel Gildea
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Weight Pushing and Binarization for Fixed-Grammar Parsing
Matt Post | Daniel Gildea
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

2008

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Parsers as language models for statistical machine translation
Matt Post | Daniel Gildea
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

Most work in syntax-based machine translation has been in translation modeling, but there are many reasons why we may instead want to focus on the language model. We experiment with parsers as language models for machine translation in a simple translation model. This approach demands much more of the language models, allowing us to isolate their strengths and weaknesses. We find that unmodified parsers do not improve BLEU scores over ngram language models, and provide an analysis of their strengths and weaknesses.
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