Matt Post


2019

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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 Volume 1: Research Track

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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.

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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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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.

2018

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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 Papers)

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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

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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.

2017

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

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

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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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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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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.

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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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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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

2015

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

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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)

2014

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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

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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)

2013

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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.

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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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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)

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)

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