Colin Cherry


2021

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Inverted Projection for Robust Speech Translation
Dirk Padfield | Colin Cherry
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

Traditional translation systems trained on written documents perform well for text-based translation but not as well for speech-based applications. We aim to adapt translation models to speech by introducing actual lexical errors from ASR and segmentation errors from automatic punctuation into our translation training data. We introduce an inverted projection approach that projects automatically detected system segments onto human transcripts and then re-segments the gold translations to align with the projected human transcripts. We demonstrate that this overcomes the train-test mismatch present in other training approaches. The new projection approach achieves gains of over 1 BLEU point over a baseline that is exposed to the human transcripts and segmentations, and these gains hold for both IWSLT data and YouTube data.

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Proceedings of the Second Workshop on Automatic Simultaneous Translation
Hua Wu | Colin Cherry | Liang Huang | Zhongjun He | Qun Liu | Maha Elbayad | Mark Liberman | Haifeng Wang | Mingbo Ma | Ruiqing Zhang
Proceedings of the Second Workshop on Automatic Simultaneous Translation

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Assessing Reference-Free Peer Evaluation for Machine Translation
Sweta Agrawal | George Foster | Markus Freitag | Colin Cherry
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been recently shown that the probabilities given by a large, multilingual model can achieve state of the art results when used as a reference-free metric. We experiment with various modifications to this model, and demonstrate that by scaling it up we can match the performance of BLEU. We analyze various potential weaknesses of the approach, and find that it is surprisingly robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities.

2020

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Re-translation versus Streaming for Simultaneous Translation
Naveen Arivazhagan | Colin Cherry | Wolfgang Macherey | George Foster
Proceedings of the 17th International Conference on Spoken Language Translation

There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available. We study a related problem in which revisions to the hypothesis beyond strictly appending words are permitted. This is suitable for applications such as live captioning an audio feed. In this setting, we compare custom streaming approaches to re-translation, a straightforward strategy where each new source token triggers a distinct translation from scratch. We find re-translation to be as good or better than state-of-the-art streaming systems, even when operating under constraints that allow very few revisions. We attribute much of this success to a previously proposed data-augmentation technique that adds prefix-pairs to the training data, which alongside wait-k inference forms a strong baseline for streaming translation. We also highlight re-translation’s ability to wrap arbitrarily powerful MT systems with an experiment showing large improvements from an upgrade to its base model.

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Human-Paraphrased References Improve Neural Machine Translation
Markus Freitag | George Foster | David Grangier | Colin Cherry
Proceedings of the Fifth Conference on Machine Translation

Automatic evaluation comparing candidate translations to human-generated paraphrases of reference translations has recently been proposed by freitag2020bleu. When used in place of original references, the paraphrased versions produce metric scores that correlate better with human judgment. This effect holds for a variety of different automatic metrics, and tends to favor natural formulations over more literal (translationese) ones. In this paper we compare the results of performing end-to-end system development using standard and paraphrased references. With state-of-the-art English-German NMT components, we show that tuning to paraphrased references produces a system that is ignificantly better according to human judgment, but 5 BLEU points worse when tested on standard references. Our work confirms the finding that paraphrased references yield metric scores that correlate better with human judgment, and demonstrates for the first time that using these scores for system development can lead to significant improvements.

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Inference Strategies for Machine Translation with Conditional Masking
Julia Kreutzer | George Foster | Colin Cherry
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Conditional masked language model (CMLM) training has proven successful for non-autoregressive and semi-autoregressive sequence generation tasks, such as machine translation. Given a trained CMLM, however, it is not clear what the best inference strategy is. We formulate masked inference as a factorization of conditional probabilities of partial sequences, show that this does not harm performance, and investigate a number of simple heuristics motivated by this perspective. We identify a thresholding strategy that has advantages over the standard “mask-predict” algorithm, and provide analyses of its behavior on machine translation tasks.

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Simultaneous Translation
Liang Huang | Colin Cherry | Mingbo Ma | Naveen Arivazhagan | Zhongjun He
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Simultaneous translation, which performs translation concurrently with the source speech, is widely useful in many scenarios such as international conferences, negotiations, press releases, legal proceedings, and medicine. This problem has long been considered one of the hardest problems in AI and one of its holy grails. Recently, with rapid improvements in machine translation, speech recognition, and speech synthesis, there has been exciting progress towards simultaneous translation. This tutorial will focus on the design and evaluation of policies for simultaneous translation, to leave attendees with a deep technical understanding of the history, the recent advances, and the remaining challenges in this field.

2019

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Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
Colin Cherry | Greg Durrett | George Foster | Reza Haffari | Shahram Khadivi | Nanyun Peng | Xiang Ren | Swabha Swayamdipta
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

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Reinforcement Learning based Curriculum Optimization for Neural Machine Translation
Gaurav Kumar | George Foster | Colin Cherry | Maxim Krikun
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)

We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT). Specifically, given a training dataset with a sentence-level feature such as noise, we seek an optimal curriculum, or order for presenting examples to the system during training. Our curriculum framework allows examples to appear an arbitrary number of times, and thus generalizes data weighting, filtering, and fine-tuning schemes. Rather than relying on prior knowledge to design a curriculum, we use reinforcement learning to learn one automatically, jointly with the NMT system, in the course of a single training run. We show that this approach can beat uniform baselines on Paracrawl and WMT English-to-French datasets by +3.4 and +1.3 BLEU respectively. Additionally, we match the performance of strong filtering baselines and hand-designed, state-of-the-art curricula.

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Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Naveen Arivazhagan | Colin Cherry | Wolfgang Macherey | Chung-Cheng Chiu | Semih Yavuz | Ruoming Pang | Wei Li | Colin Raffel
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios. Simultaneous systems must carefully schedule their reading of the source sentence to balance quality against latency. We present the first simultaneous translation system to learn an adaptive schedule jointly with a neural machine translation (NMT) model that attends over all source tokens read thus far. We do so by introducing Monotonic Infinite Lookback (MILk) attention, which maintains both a hard, monotonic attention head to schedule the reading of the source sentence, and a soft attention head that extends from the monotonic head back to the beginning of the source. We show that MILk’s adaptive schedule allows it to arrive at latency-quality trade-offs that are favorable to those of a recently proposed wait-k strategy for many latency values.

2018

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Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
Colin Cherry | Graham Neubig
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 Workshop on Deep Learning Approaches for Low-Resource NLP
Reza Haffari | Colin Cherry | George Foster | Shahram Khadivi | Bahar Salehi
Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP

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Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Colin Cherry | George Foster | Ankur Bapna | Orhan Firat | Wolfgang Macherey
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by eliminating hyper-parameters and manual feature engineering. However, it results in longer sequences in which each symbol contains less information, creating both modeling and computational challenges. In this paper, we show that the modeling problem can be solved by standard sequence-to-sequence architectures of sufficient depth, and that deep models operating at the character level outperform identical models operating over word fragments. This result implies that alternative architectures for handling character input are better viewed as methods for reducing computation time than as improved ways of modeling longer sequences. From this perspective, we evaluate several techniques for character-level NMT, verify that they do not match the performance of our deep character baseline model, and evaluate the performance versus computation time tradeoffs they offer. Within this framework, we also perform the first evaluation for NMT of conditional computation over time, in which the model learns which timesteps can be skipped, rather than having them be dictated by a fixed schedule specified before training begins.

2017

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Cost Weighting for Neural Machine Translation Domain Adaptation
Boxing Chen | Colin Cherry | George Foster | Samuel Larkin
Proceedings of the First Workshop on Neural Machine Translation

In this paper, we propose a new domain adaptation technique for neural machine translation called cost weighting, which is appropriate for adaptation scenarios in which a small in-domain data set and a large general-domain data set are available. Cost weighting incorporates a domain classifier into the neural machine translation training algorithm, using features derived from the encoder representation in order to distinguish in-domain from out-of-domain data. Classifier probabilities are used to weight sentences according to their domain similarity when updating the parameters of the neural translation model. We compare cost weighting to two traditional domain adaptation techniques developed for statistical machine translation: data selection and sub-corpus weighting. Experiments on two large-data tasks show that both the traditional techniques and our novel proposal lead to significant gains, with cost weighting outperforming the traditional methods.

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NRC Machine Translation System for WMT 2017
Chi-kiu Lo | Boxing Chen | Colin Cherry | George Foster | Samuel Larkin | Darlene Stewart | Roland Kuhn
Proceedings of the Second Conference on Machine Translation

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A Challenge Set Approach to Evaluating Machine Translation
Pierre Isabelle | Colin Cherry | George Foster
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these questions with a challenge set approach to translation evaluation and error analysis. A challenge set consists of a small set of sentences, each hand-designed to probe a system’s capacity to bridge a particular structural divergence between languages. To exemplify this approach, we present an English-French challenge set, and use it to analyze phrase-based and neural systems. The resulting analysis provides not only a more fine-grained picture of the strengths of neural systems, but also insight into which linguistic phenomena remain out of reach.

2016

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Bilingual Methods for Adaptive Training Data Selection for Machine Translation
Boxing Chen | Roland Kuhn | George Foster | Colin Cherry | Fei Huang
Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track

In this paper, we propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual corpus. In earlier work, we devised a data selection method based on semi-supervised convolutional neural networks (SSCNNs). The new method, Bi-SSCNN, is based on bitokens, which use bilingual information. When the new methods are tested on two translation tasks (Chinese-to-English and Arabic-to-English), they significantly outperform the other three data selection methods in the experiments. We also show that the BiSSCNN method is much more effective than other methods in preventing noisy sentence pairs from being chosen for training. More interestingly, this method only needs a tiny amount of in-domain data to train the selection model, which makes fine-grained topic-dependent translation adaptation possible. In the follow-up experiments, we find that neural machine translation (NMT) is more sensitive to noisy data than statistical machine translation (SMT). Therefore, Bi-SSCNN which can effectively screen out noisy sentence pairs, can benefit NMT much more than SMT.We observed a BLEU improvement over 3 points on an English-to-French WMT task when Bi-SSCNNs were used.

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A Dataset for Detecting Stance in Tweets
Saif Mohammad | Svetlana Kiritchenko | Parinaz Sobhani | Xiaodan Zhu | Colin Cherry
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We can often detect from a person’s utterances whether he/she is in favor of or against a given target entity (a product, topic, another person, etc.). Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets of interest―their stance. The targets of interest may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. The data pertains to six targets of interest commonly known and debated in the United States. Apart from stance, the tweets are also annotated for whether the target of interest is the target of opinion in the tweet. The annotations were performed by crowdsourcing. Several techniques were employed to encourage high-quality annotations (for example, providing clear and simple instructions) and to identify and discard poor annotations (for example, using a small set of check questions annotated by the authors). This Stance Dataset, which was subsequently also annotated for sentiment, can be used to better understand the relationship between stance, sentiment, entity relationships, and textual inference.

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NRC Russian-English Machine Translation System for WMT 2016
Chi-kiu Lo | Colin Cherry | George Foster | Darlene Stewart | Rabib Islam | Anna Kazantseva | Roland Kuhn
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation
Colin Cherry
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Integrating Morphological Desegmentation into Phrase-based Decoding
Mohammad Salameh | Colin Cherry | Grzegorz Kondrak
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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SemEval-2016 Task 6: Detecting Stance in Tweets
Saif Mohammad | Svetlana Kiritchenko | Parinaz Sobhani | Xiaodan Zhu | Colin Cherry
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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What Matters Most in Morphologically Segmented SMT Models?
Mohammad Salameh | Colin Cherry | Grzegorz Kondrak
Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Morpho-syntactic Regularities in Continuous Word Representations: A multilingual study.
Garrett Nicolai | Colin Cherry | Grzegorz Kondrak
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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NRC: Infused Phrase Vectors for Named Entity Recognition in Twitter
Colin Cherry | Hongyu Guo | Chengbi Dai
Proceedings of the Workshop on Noisy User-generated Text

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The Unreasonable Effectiveness of Word Representations for Twitter Named Entity Recognition
Colin Cherry | Hongyu Guo
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Inflection Generation as Discriminative String Transduction
Garrett Nicolai | Colin Cherry | Grzegorz Kondrak
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU
Boxing Chen | Colin Cherry
Proceedings of the Ninth Workshop on Statistical Machine Translation

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NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews
Svetlana Kiritchenko | Xiaodan Zhu | Colin Cherry | Saif Mohammad
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Lattice Desegmentation for Statistical Machine Translation
Mohammad Salameh | Colin Cherry | Grzegorz Kondrak
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Regularized Minimum Error Rate Training
Michel Galley | Chris Quirk | Colin Cherry | Kristina Toutanova
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Improved Reordering for Phrase-Based Translation using Sparse Features
Colin Cherry
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Reversing Morphological Tokenization in English-to-Arabic SMT
Mohammad Salameh | Colin Cherry | Grzegorz Kondrak
Proceedings of the 2013 NAACL HLT Student Research Workshop

2012

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On Hierarchical Re-ordering and Permutation Parsing for Phrase-based Decoding
Colin Cherry | Robert C. Moore | Chris Quirk
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Paraphrasing for Style
Wei Xu | Alan Ritter | Bill Dolan | Ralph Grishman | Colin Cherry
Proceedings of COLING 2012

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Batch Tuning Strategies for Statistical Machine Translation
Colin Cherry | George Foster
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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MSR SPLAT, a language analysis toolkit
Chris Quirk | Pallavi Choudhury | Jianfeng Gao | Hisami Suzuki | Kristina Toutanova | Michael Gamon | Wen-tau Yih | Colin Cherry | Lucy Vanderwende
Proceedings of the Demonstration Session at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Data-Driven Response Generation in Social Media
Alan Ritter | Colin Cherry | William B. Dolan
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Indexing Spoken Documents with Hierarchical Semantic Structures: Semantic Tree-to-string Alignment Models
Xiaodan Zhu | Colin Cherry | Gerald Penn
Proceedings of 5th International Joint Conference on Natural Language Processing

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Lexically-Triggered Hidden Markov Models for Clinical Document Coding
Svetlana Kiritchenko | Colin Cherry
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Joint Training of Dependency Parsing Filters through Latent Support Vector Machines
Colin Cherry | Shane Bergsma
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Unsupervised Modeling of Twitter Conversations
Alan Ritter | Colin Cherry | Bill Dolan
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Integrating Joint n-gram Features into a Discriminative Training Framework
Sittichai Jiampojamarn | Colin Cherry | Grzegorz Kondrak
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Fast and Accurate Arc Filtering for Dependency Parsing
Shane Bergsma | Colin Cherry
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Imposing Hierarchical Browsing Structures onto Spoken Documents
Xiaodan Zhu | Colin Cherry | Gerald Penn
Coling 2010: Posters

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Book Review: Statistical Machine Translation by Philipp Koehn
Colin Cherry
Computational Linguistics, Volume 36, Issue 4 - December 2010

2009

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A global model for joint lemmatization and part-of-speech prediction
Kristina Toutanova | Colin Cherry
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Discriminative Substring Decoding for Transliteration
Colin Cherry | Hisami Suzuki
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Unsupervised Morphological Segmentation with Log-Linear Models
Hoifung Poon | Colin Cherry | Kristina Toutanova
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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On the Syllabification of Phonemes
Susan Bartlett | Grzegorz Kondrak | Colin Cherry
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Cohesive Constraints in A Beam Search Phrase-based Decoder
Nguyen Bach | Stephan Vogel | Colin Cherry
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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NEWS 2009 Machine Transliteration Shared Task System Description: Transliteration with Letter-to-Phoneme Technology
Colin Cherry | Hisami Suzuki
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

2008

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Discriminative, Syntactic Language Modeling through Latent SVMs
Colin Cherry | Chris Quirk
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

We construct a discriminative, syntactic language model (LM) by using a latent support vector machine (SVM) to train an unlexicalized parser to judge sentences. That is, the parser is optimized so that correct sentences receive high-scoring trees, while incorrect sentences do not. Because of this alternative objective, the parser can be trained with only a part-of-speech dictionary and binary-labeled sentences. We follow the paradigm of discriminative language modeling with pseudo-negative examples (Okanohara and Tsujii, 2007), and demonstrate significant improvements in distinguishing real sentences from pseudo-negatives. We also investigate the related task of separating machine-translation (MT) outputs from reference translations, again showing large improvements. Finally, we test our LM in MT reranking, and investigate the language-modeling parser in the context of unsupervised parsing.

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Cohesive Phrase-Based Decoding for Statistical Machine Translation
Colin Cherry
Proceedings of ACL-08: HLT

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Automatic Syllabification with Structured SVMs for Letter-to-Phoneme Conversion
Susan Bartlett | Grzegorz Kondrak | Colin Cherry
Proceedings of ACL-08: HLT

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Joint Processing and Discriminative Training for Letter-to-Phoneme Conversion
Sittichai Jiampojamarn | Colin Cherry | Grzegorz Kondrak
Proceedings of ACL-08: HLT

2007

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Inversion Transduction Grammar for Joint Phrasal Translation Modeling
Colin Cherry | Dekang Lin
Proceedings of SSST, NAACL-HLT 2007 / AMTA Workshop on Syntax and Structure in Statistical Translation

2006

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Soft Syntactic Constraints for Word Alignment through Discriminative Training
Colin Cherry | Dekang Lin
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Improved Large Margin Dependency Parsing via Local Constraints and Laplacian Regularization
Qin Iris Wang | Colin Cherry | Dan Lizotte | Dale Schuurmans
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

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Biomedical Term Recognition with the Perceptron HMM Algorithm
Sittichai Jiampojamarn | Grzegorz Kondrak | Colin Cherry
Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology

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A Comparison of Syntactically Motivated Word Alignment Spaces
Colin Cherry | Dekang Lin
11th Conference of the European Chapter of the Association for Computational Linguistics

2005

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An Expectation Maximization Approach to Pronoun Resolution
Colin Cherry | Shane Bergsma
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

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Dependency Treelet Translation: Syntactically Informed Phrasal SMT
Chris Quirk | Arul Menezes | Colin Cherry
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2003

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Word Alignment with Cohesion Constraint
Dekang Lin | Colin Cherry
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

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A Probability Model to Improve Word Alignment
Colin Cherry | Dekang Lin
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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ProAlign: Shared Task System Description
Dekang Lin | Colin Cherry
Proceedings of the HLT-NAACL 2003 Workshop on Building and Using Parallel Texts: Data Driven Machine Translation and Beyond