Andrew Finch


2020

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Proceedings of the Fourth Workshop on Neural Generation and Translation
Alexandra Birch | Andrew Finch | Hiroaki Hayashi | Kenneth Heafield | Marcin Junczys-Dowmunt | Ioannis Konstas | Xian Li | Graham Neubig | Yusuke Oda
Proceedings of the Fourth Workshop on Neural Generation and Translation

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Findings of the Fourth Workshop on Neural Generation and Translation
Kenneth Heafield | Hiroaki Hayashi | Yusuke Oda | Ioannis Konstas | Andrew Finch | Graham Neubig | Xian Li | Alexandra Birch
Proceedings of the Fourth Workshop on Neural Generation and Translation

We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the three shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language and 3) STAPLE task: creation of as many possible translations of a given input text. This last shared task was organised by Duolingo.

2019

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Proceedings of the 3rd Workshop on Neural Generation and Translation
Alexandra Birch | Andrew Finch | Hiroaki Hayashi | Ioannis Konstas | Thang Luong | Graham Neubig | Yusuke Oda | Katsuhito Sudoh
Proceedings of the 3rd Workshop on Neural Generation and Translation

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Findings of the Third Workshop on Neural Generation and Translation
Hiroaki Hayashi | Yusuke Oda | Alexandra Birch | Ioannis Konstas | Andrew Finch | Minh-Thang Luong | Graham Neubig | Katsuhito Sudoh
Proceedings of the 3rd Workshop on Neural Generation and Translation

This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.

2018

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Proceedings of the 2nd Workshop on Neural Machine Translation and Generation
Alexandra Birch | Andrew Finch | Thang Luong | Graham Neubig | Yusuke Oda
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

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Findings of the Second Workshop on Neural Machine Translation and Generation
Alexandra Birch | Andrew Finch | Minh-Thang Luong | Graham Neubig | Yusuke Oda
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop’s shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient.

2017

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Proceedings of the First Workshop on Neural Machine Translation
Thang Luong | Alexandra Birch | Graham Neubig | Andrew Finch
Proceedings of the First Workshop on Neural Machine Translation

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Sentence Embedding for Neural Machine Translation Domain Adaptation
Rui Wang | Andrew Finch | Masao Utiyama | Eiichiro Sumita
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Although new corpora are becoming increasingly available for machine translation, only those that belong to the same or similar domains are typically able to improve translation performance. Recently Neural Machine Translation (NMT) has become prominent in the field. However, most of the existing domain adaptation methods only focus on phrase-based machine translation. In this paper, we exploit the NMT’s internal embedding of the source sentence and use the sentence embedding similarity to select the sentences which are close to in-domain data. The empirical adaptation results on the IWSLT English-French and NIST Chinese-English tasks show that the proposed methods can substantially improve NMT performance by 2.4-9.0 BLEU points, outperforming the existing state-of-the-art baseline by 2.3-4.5 BLEU points.

2016

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Introducing the Asian Language Treebank (ALT)
Ye Kyaw Thu | Win Pa Pa | Masao Utiyama | Andrew Finch | Eiichiro Sumita
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper introduces the ALT project initiated by the Advanced Speech Translation Research and Development Promotion Center (ASTREC), NICT, Kyoto, Japan. The aim of this project is to accelerate NLP research for Asian languages such as Indonesian, Japanese, Khmer, Laos, Malay, Myanmar, Philippine, Thai and Vietnamese. The original resource for this project was English articles that were randomly selected from Wikinews. The project has so far created a corpus for Myanmar and will extend in scope to include other languages in the near future. A 20000-sentence corpus of Myanmar that has been manually translated from an English corpus has been word segmented, word aligned, part-of-speech tagged and constituency parsed by human annotators. In this paper, we present the implementation steps for creating the treebank in detail, including a description of the ALT web-based treebanking tool. Moreover, we report statistics on the annotation quality of the Myanmar treebank created so far.

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Target-Bidirectional Neural Models for Machine Transliteration
Andrew Finch | Lemao Liu | Xiaolin Wang | Eiichiro Sumita
Proceedings of the Sixth Named Entity Workshop

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An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation
Xiaolin Wang | Andrew Finch | Masao Utiyama | Eiichiro Sumita
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

Simultaneous interpretation is a very challenging application of machine translation in which the input is a stream of words from a speech recognition engine. The key problem is how to segment the stream in an online manner into units suitable for translation. The segmentation process proceeds by calculating a confidence score for each word that indicates the soundness of placing a sentence boundary after it, and then heuristics are employed to determine the position of the boundaries. Multiple variants of the confidence scoring method and segmentation heuristics were studied. Experimental results show that the best performing strategy is not only efficient in terms of average latency per word, but also achieved end-to-end translation quality close to an offline baseline, and close to oracle segmentation.

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Agreement on Target-bidirectional Neural Machine Translation
Lemao Liu | Masao Utiyama | Andrew Finch | Eiichiro Sumita
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Interlocking Phrases in Phrase-based Statistical Machine Translation
Ye Kyaw Thu | Andrew Finch | Eiichiro Sumita
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Neural Machine Translation with Supervised Attention
Lemao Liu | Masao Utiyama | Andrew Finch | Eiichiro Sumita
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

The attention mechanism is appealing for neural machine translation, since it is able to dynamically encode a source sentence by generating a alignment between a target word and source words. Unfortunately, it has been proved to be worse than conventional alignment models in alignment accuracy. In this paper, we analyze and explain this issue from the point view of reordering, and propose a supervised attention which is learned with guidance from conventional alignment models. Experiments on two Chinese-to-English translation tasks show that the supervised attention mechanism yields better alignments leading to substantial gains over the standard attention based NMT.

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A Prototype Automatic Simultaneous Interpretation System
Xiaolin Wang | Andrew Finch | Masao Utiyama | Eiichiro Sumita
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

Simultaneous interpretation allows people to communicate spontaneously across language boundaries, but such services are prohibitively expensive for the general public. This paper presents a fully automatic simultaneous interpretation system to address this problem. Though the development is still at an early stage, the system is capable of keeping up with the fastest of the TED speakers while at the same time delivering high-quality translations. We believe that the system will become an effective tool for facilitating cross-lingual communication in the future.

2015

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A Large-scale Study of Statistical Machine Translation Methods for Khmer Language
Ye Kyaw Thu | Vichet Chea | Andrew Finch | Masao Utiyama | Eiichiro Sumita
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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Hierarchical Phrase-based Stream Decoding
Andrew Finch | Xiaolin Wang | Masao Utiyama | Eiichiro Sumita
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Leave-one-out Word Alignment without Garbage Collector Effects
Xiaolin Wang | Masao Utiyama | Andrew Finch | Taro Watanabe | Eiichiro Sumita
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Neural Network Transduction Models in Transliteration Generation
Andrew Finch | Lemao Liu | Xiaolin Wang | Eiichiro Sumita
Proceedings of the Fifth Named Entity Workshop

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Learning bilingual phrase representations with recurrent neural networks
Hideya Mino | Andrew Finch | Eiichiro Sumita
Proceedings of Machine Translation Summit XV: Papers

2014

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Integrating Dictionaries into an Unsupervised Model for Myanmar Word Segmentation
Ye Kyaw Thu | Andrew Finch | Eiichiro Sumita | Yoshinori Sagisaka
Proceedings of the Fifth Workshop on South and Southeast Asian Natural Language Processing

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The NICT translation system for IWSLT 2014
Xiaolin Wang | Andrew Finch | Masao Utiyama | Taro Watanabe | Eiichiro Sumita
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes NICT’s participation in the IWSLT 2014 evaluation campaign for the TED Chinese-English translation shared-task. Our approach used a combination of phrase-based and hierarchical statistical machine translation (SMT) systems. Our focus was in several areas, specifically system combination, word alignment, and various language modeling techniques including the use of neural network joint models. Our experiments on the test set from the 2013 shared task, showed that an improvement in BLEU score can be gained in translation performance through all of these techniques, with the largest improvements coming from using large data sizes to train the language model.

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Empircal dependency-based head finalization for statistical Chinese-, English-, and French-to-Myanmar (Burmese) machine translation
Chenchen Ding | Ye Kyaw Thu | Masao Utiyama | Andrew Finch | Eiichiro Sumita
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

We conduct dependency-based head finalization for statistical machine translation (SMT) for Myanmar (Burmese). Although Myanmar is an understudied language, linguistically it is a head-final language with similar syntax to Japanese and Korean. So, applying the efficient techniques of Japanese and Korean processing to Myanmar is a natural idea. Our approach is a combination of two approaches. The first is a head-driven phrase structure grammar (HPSG) based head finalization for English-to-Japanese translation, the second is dependency-based pre-ordering originally designed for English-to-Korean translation. We experiment on Chinese-, English-, and French-to-Myanmar translation, using a statistical pre-ordering approach as a comparison method. Experimental results show the dependency-based head finalization was able to consistently improve a baseline SMT system, for different source languages and different segmentation schemes for the Myanmar language.

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An exploration of segmentation strategies in stream decoding
Andrew Finch | Xiaolin Wang | Eiichiro Sumita
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

In this paper we explore segmentation strategies for the stream decoder a method for decoding from a continuous stream of input tokens, rather than the traditional method of decoding from sentence segmented text. The behavior of the decoder is analyzed and modifications to the decoding algorithm are proposed to improve its performance. The experimental results show our proposed decoding strategies to be effective, and add support to the original findings that this approach is capable of approaching the performance of the underlying phrase-based machine translation decoder, at useful levels of latency. Our experiments evaluated the stream decoder on a broader set of language pairs than in previous work. We found most European language pairs were similar in character, and report results on English-Chinese and English-German pairs which are of interest due to the reordering required.

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Refining Word Segmentation Using a Manually Aligned Corpus for Statistical Machine Translation
Xiaolin Wang | Masao Utiyama | Andrew Finch | Eiichiro Sumita
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Empirical Study of Unsupervised Chinese Word Segmentation Methods for SMT on Large-scale Corpora
Xiaolin Wang | Masao Utiyama | Andrew Finch | Eiichiro Sumita
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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A Tightly-coupled Unsupervised Clustering and Bilingual Alignment Model for Transliteration
Tingting Li | Tiejun Zhao | Andrew Finch | Chunyue Zhang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Inducing Romanization Systems
Keiko Taguchi | Andrew Finch | Seiichi Yamamoto | Eiichiro Sumita
Proceedings of Machine Translation Summit XIV: Papers

2012

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The NICT translation system for IWSLT 2012
Andrew Finch | Ohnmar Htun | Eiichiro Sumita
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

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Rescoring a Phrase-based Machine Transliteration System with Recurrent Neural Network Language Models
Andrew Finch | Paul Dixon | Eiichiro Sumita
Proceedings of the 4th Named Entity Workshop (NEWS) 2012

2011

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Dialect Translation: Integrating Bayesian Co-segmentation Models with Pivot-based SMT
Michael Paul | Andrew Finch | Paul R. Dixon | Eiichiro Sumita
Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties

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Integrating Models Derived from non-Parametric Bayesian Co-segmentation into a Statistical Machine Transliteration System
Andrew Finch | Paul Dixon | Eiichiro Sumita
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

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Using Features from a Bilingual Alignment Model in Transliteration Mining
Takaaki Fukunishi | Andrew Finch | Seiichi Yamamoto | Eiichiro Sumita
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

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The NICT translation system for IWSLT 2011
Andrew Finch | Chooi-Ling Goh | Graham Neubig | Eiichiro Sumita
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes NICT’s participation in the IWSLT 2011 evaluation campaign for the TED speech translation ChineseEnglish shared-task. Our approach was based on a phrasebased statistical machine translation system that was augmented in two ways. Firstly we introduced rule-based re-ordering constraints on the decoding. This consisted of a set of rules that were used to segment the input utterances into segments that could be decoded almost independently. This idea here being that constraining the decoding process in this manner would greatly reduce the search space of the decoder, and cut out many possibilities for error while at the same time allowing for a correct output to be generated. The rules we used exploit punctuation and spacing in the input utterances, and we use these positions to delimit our segments. Not all punctuation/spacing positions were used as segment boundaries, and the set of used positions were determined by a set of linguistically-based heuristics. Secondly we used two heterogeneous methods to build the translation model, and lexical reordering model for our systems. The first method employed the popular method of using GIZA++ for alignment in combination with phraseextraction heuristics. The second method used a recentlydeveloped Bayesian alignment technique that is able to perform both phrase-to-phrase alignment and phrase pair extraction within a single unsupervised process. The models produced by this type of alignment technique are typically very compact whilst at the same time maintaining a high level of translation quality. We evaluated both of these methods of translation model construction in isolation, and our results show their performance is comparable. We also integrated both models by linear interpolation to obtain a model that outperforms either component. Finally, we added an indicator feature into the log-linear model to indicate those phrases that were in the intersection of the two translation models. The addition of this feature was also able to provide a small improvement in performance.

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Investigation of the effects of ASR tuning on speech translation performance
Paul R. Dixon | Andrew Finch | Chiori Hori | Hideki Kashioka
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper we describe some of our recent investigations into ASR and SMT coupling issues from an ASR perspective. Our study was motivated by several areas: Firstly, to understand how standard ASR tuning procedures effect the SMT performance and whether it is safe to perform this tuning in isolation. Secondly, to investigate how vocabulary and segmentation mismatches between the ASR and SMT system effect the performance. Thirdly, to uncover any practical issues that arise when using a WFST based speech decoder for tight coupling as opposed to a more traditional tree-search decoding architecture. On the IWSLT07 Japanese-English task we found that larger language model weights only helped the SMT performance when the ASR decoder was tuned in a sub-optimal manner. When we considered the performance with suitable wide beams that ensured the ASR accuracy had converged we observed the language model weight had little influence on the SMT BLEU scores. After the construction of the phrase table the actual SMT vocabulary can be less than the training data vocabulary. By reducing the ASR lexicon to only cover the words the SMT system could accept, we found this lead to an increase in the ASR error rates, however the SMT BLEU scores were nearly unchanged. From a practical point of view this is a useful result as it means we can significantly reduce the memory footprint of the ASR system. We also investigated coupling WFST based ASR to a simple WFST based translation decoder and found it was crucial to perform phrase table expansion to avoid OOV problems. For the WFST translation decoder we describe a semiring based approach for optimizing the log-linear weights.

2010

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Integration of Multiple Bilingually-Learned Segmentation Schemes into Statistical Machine Translation
Michael Paul | Andrew Finch | Eiichiro Sumita
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Transliteration Using a Phrase-Based Statistical Machine Translation System to Re-Score the Output of a Joint Multigram Model
Andrew Finch | Eiichiro Sumita
Proceedings of the 2010 Named Entities Workshop

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Syntactic Constraints on Phrase Extraction for Phrase-Based Machine Translation
Hailong Cao | Andrew Finch | Eiichiro Sumita
Proceedings of the 4th Workshop on Syntax and Structure in Statistical Translation

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The NICT translation system for IWSLT 2010
Chooi-Ling Goh | Taro Watanabe | Michael Paul | Andrew Finch | Eiichiro Sumita
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes NICT’s participation in the IWSLT 2010 evaluation campaign for the DIALOG translation (Chinese-English) and the BTEC (French-English) translation shared-tasks. For the DIALOG translation, the main challenge to this task is applying context information during translation. Context information can be used to decide on word choice and also to replace missing information during translation. We applied discriminative reranking using contextual information as additional features. In order to provide more choices for re-ranking, we generated n-best lists from multiple phrase-based statistical machine translation systems that varied in the type of Chinese word segmentation schemes used. We also built a model that merged the phrase tables generated by the different segmentation schemes. Furthermore, we used a lattice-based system combination model to combine the output from different systems. A combination of all of these systems was used to produce the n-best lists for re-ranking. For the BTEC task, a general approach that used latticebased system combination of two systems, a standard phrasebased system and a hierarchical phrase-based system, was taken. We also tried to process some unknown words by replacing them with the same words but different inflections that are known to the system.

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A Bayesian model of bilingual segmentation for transliteration
Andrew Finch | Eiichiro Sumita
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

2009

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Bidirectional Phrase-based Statistical Machine Translation
Andrew Finch | Eiichiro Sumita
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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NICT@WMT09: Model Adaptation and Transliteration for Spanish-English SMT
Michael Paul | Andrew Finch | Eiichiro Sumita
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Transliteration by Bidirectional Statistical Machine Translation
Andrew Finch | Eiichiro Sumita
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

2008

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Dynamic Model Interpolation for Statistical Machine Translation
Andrew Finch | Eiichiro Sumita
Proceedings of the Third Workshop on Statistical Machine Translation

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Phrase-based Machine Transliteration
Andrew Finch | Eiichiro Sumita
Proceedings of the Workshop on Technologies and Corpora for Asia-Pacific Speech Translation (TCAST)

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The NICT/ATR speech translation system for IWSLT 2008.
Masao Utiyama | Andrew Finch | Hideo Okuma | Michael Paul | Hailong Cao | Hirofumi Yamamoto | Keiji Yasuda | Eiichiro Sumita
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the National Institute of Information and Communications Technology/Advanced Telecommunications Research Institute International (NICT/ATR) statistical machine translation (SMT) system used for the IWSLT 2008 evaluation campaign. We participated in the Chinese–English (Challenge Task), English–Chinese (Challenge Task), Chinese–English (BTEC Task), Chinese–Spanish (BTEC Task), and Chinese–English–Spanish (PIVOT Task) translation tasks. In the English–Chinese translation Challenge Task, we focused on exploring various factors for the English–Chinese translation because the research on the translation of English–Chinese is scarce compared to the opposite direction. In the Chinese–English translation Challenge Task, we employed a novel clustering method, where training sentences similar to the development data in terms of the word error rate formed a cluster. In the pivot translation task, we integrated two strategies for pivot translation by linear interpolation.

2007

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Reducing human assessment of machine translation quality to binary classifiers
Michael Paul | Andrew Finch | Eiichiro Sumita
Proceedings of the 11th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages: Papers

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The NICT/ATR speech translation system for IWSLT 2007
Andrew Finch | Etienne Denoual | Hideo Okuma | Michael Paul | Hirofumi Yamamoto | Keiji Yasuda | Ruiqiang Zhang | Eiichiro Sumita
Proceedings of the Fourth International Workshop on Spoken Language Translation

This paper describes the NiCT-ATR statistical machine translation (SMT) system used for the IWSLT 2007 evaluation campaign. We participated in three of the four language pair translation tasks (CE, JE, and IE). We used a phrase-based SMT system using log-linear feature models for all tracks. This year we decoded from the ASR n-best lists in the JE track and found a gain in performance. We also applied some new techniques to facilitate the use of out-of-domain external resources by model combination and also by utilizing a huge corpus of n-grams provided by Google Inc.. Using these resources gave mixed results that depended on the technique also the language pair however, in some cases we achieved consistently positive results. The results from model-interpolation in particular were very promising.

2006

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Using Lexical Dependency and Ontological Knowledge to Improve a Detailed Syntactic and Semantic Tagger of English
Andrew Finch | Ezra Black | Young-Sook Hwang | Eiichiro Sumita
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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The NiCT-ATR statistical machine translation system for IWSLT 2006
Ruiqiang Zhang | Hirofumi Yamamoto | Michael Paul | Hideo Okuma | Keiji Yasuda | Yves Lepage | Etienne Denoual | Daichi Mochihashi | Andrew Finch | Eiichiro Sumita
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

2005

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Using Machine Translation Evaluation Techniques to Determine Sentence-level Semantic Equivalence
Andrew Finch | Young-Sook Hwang | Eiichiro Sumita
Proceedings of the Third International Workshop on Paraphrasing (IWP2005)

2004

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How Does Automatic Machine Translation Evaluation Correlate with Human Scoring as the Number of Reference Translations Increases?
Andrew Finch | Yasuhiro Akiba | Eiichiro Sumita
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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EBMT, SMT, hybrid and more: ATR spoken language translation system
Eiichiro Sumita | Yasuhiro Akiba | Takao Doi | Andrew Finch | Kenji Imamura | Hideo Okuma | Michael Paul | Mitsuo Shimohata | Taro Watanabe
Proceedings of the First International Workshop on Spoken Language Translation: Evaluation Campaign

2003

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A corpus-centered approach to spoken language translation
Eiichiro Sumita | Yasuhiro Akiba | Takao Doi | Andrew Finch | Kenji Imamura | Michael Paul | Mitsuo Shimohata | Taro Watanabe
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Beyond Tag Trigrams: New Local Features for Tagging
Andrew Finch | Ezra Black | Ringo Wathelet
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

1999

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Applying Extrasentential Context To Maximum Entropy Based Tagging With A Large Semantic And Syntactic Tagset
Ezra Black | Andrew Finch | Ruiqiang Zhang
1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

1998

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Trigger-Pair Predictors in Parsing and Tagging
Ezra Black | Andrew Finch | Hideki Kashioka
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Use of Mutual Information Based Character Clusters in Dictionary-less Morphological Analysis of Japanese
Hideki Kashioka | Yasuhiro Kawata | Yumiko Kinjo | Andrew Finch | Ezra W. Black
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Trigger-Pair Predictors in Parsing and Tagging
Ezra Black | Andrew Finch | Hideki Kashioka
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Use of Mutual Information Based Character Clusters in Dictionary-less Morphological Analysis of Japanese
Hideki Kashioka | Yasuhiro Kawata | Yumiko Kinjo | Andrew Finch | Ezra W. Black
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1