Joel Tetreault

Also published as: Joel R. Tetreault


2020

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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Dan Jurafsky | Joyce Chai | Natalie Schluter | Joel Tetreault
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events
Claire Bonial | Tommaso Caselli | Snigdha Chaturvedi | Elizabeth Clark | Ruihong Huang | Mohit Iyyer | Alejandro Jaimes | Heng Ji | Lara J. Martin | Ben Miller | Teruko Mitamura | Nanyun Peng | Joel Tetreault
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events

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The ApposCorpus: a new multilingual, multi-domain dataset for factual appositive generation
Yova Kementchedjhieva | Di Lu | Joel Tetreault
Proceedings of the 28th International Conference on Computational Linguistics

News articles, image captions, product reviews and many other texts mention people and organizations whose name recognition could vary for different audiences. In such cases, background information about the named entities could be provided in the form of an appositive noun phrase, either written by a human or generated automatically. We expand on the previous work in appositive generation with a new, more realistic, end-to-end definition of the task, instantiated by a dataset that spans four languages (English, Spanish, German and Polish), two entity types (person and organization) and two domains (Wikipedia and News). We carry out an extensive analysis of the data and the task, pointing to the various modeling challenges it poses. The results we obtain with standard language generation methods show that the task is indeed non-trivial, and leaves plenty of room for improvement.

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Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing
Anne Lauscher | Lily Ng | Courtney Napoles | Joel Tetreault
Proceedings of the 28th International Conference on Computational Linguistics

Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argumentation theory. However, a large-scale theory-based corpus and corresponding computational models are missing. We fill this gap by conducting an extensive analysis covering three diverse domains of online argumentative writing and presenting GAQCorpus: the first large-scale English multi-domain (community Q&A forums, debate forums, review forums) corpus annotated with theory-based AQ scores. We then propose the first computational approaches to theory-based assessment, which can serve as strong baselines for future work. We demonstrate the feasibility of large-scale AQ annotation, show that exploiting relations between dimensions yields performance improvements, and explore the synergies between theory-based prediction and practical AQ assessment.

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Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment
Lily Ng | Anne Lauscher | Joel Tetreault | Courtney Napoles
Proceedings of the 7th Workshop on Argument Mining

Computational models of argument quality (AQ) have focused primarily on assessing the overall quality or just one specific characteristic of an argument, such as its convincingness or its clarity. However, previous work has claimed that assessment based on theoretical dimensions of argumentation could benefit writers, but developing such models has been limited by the lack of annotated data. In this work, we describe GAQCorpus, the first large, domain-diverse annotated corpus of theory-based AQ. We discuss how we designed the annotation task to reliably collect a large number of judgments with crowdsourcing, formulating theory-based guidelines that helped make subjective judgments of AQ more objective. We demonstrate how to identify arguments and adapt the annotation task for three diverse domains. Our work will inform research on theory-based argumentation annotation and enable the creation of more diverse corpora to support computational AQ assessment.

2019

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Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses
Courtney Napoles | Maria Nădejde | Joel Tetreault
Transactions of the Association for Computational Linguistics, Volume 7

Until now, grammatical error correction (GEC) has been primarily evaluated on text written by non-native English speakers, with a focus on student essays. This paper enables GEC development on text written by native speakers by providing a new data set and metric. We present a multiple-reference test corpus for GEC that includes 4,000 sentences in two new domains (formal and informal writing by native English speakers) and 2,000 sentences from a diverse set of non-native student writing. We also collect human judgments of several GEC systems on this new test set and perform a meta-evaluation, assessing how reliable automatic metrics are across these domains. We find that commonly used GEC metrics have inconsistent performance across domains, and therefore we propose a new ensemble metric that is robust on all three domains of text.

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Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1
Maria Nadejde | Joel Tetreault
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Grammar error correction (GEC) systems have become ubiquitous in a variety of software applications, and have started to approach human-level performance for some datasets. However, very little is known about how to efficiently personalize these systems to the user’s characteristics, such as their proficiency level and first language, or to emerging domains of text. We present the first results on adapting a general purpose neural GEC system to both the proficiency level and the first language of a writer, using only a few thousand annotated sentences. Our study is the broadest of its kind, covering five proficiency levels and twelve different languages, and comparing three different adaptation scenarios: adapting to the proficiency level only, to the first language only, or to both aspects simultaneously. We show that tailoring to both scenarios achieves the largest performance improvement (3.6 F0.5) relative to a strong baseline.

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This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
Rui Zhang | Joel Tetreault
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Given the overwhelming number of emails, an effective subject line becomes essential to better inform the recipient of the email’s content. In this paper, we propose and study the task of email subject line generation: automatically generating an email subject line from the email body. We create the first dataset for this task and find that email subject line generation favor extremely abstractive summary which differentiates it from news headline generation or news single document summarization. We then develop a novel deep learning method and compare it to several baselines as well as recent state-of-the-art text summarization systems. We also investigate the efficacy of several automatic metrics based on correlations with human judgments and propose a new automatic evaluation metric. Our system outperforms competitive baselines given both automatic and human evaluations. To our knowledge, this is the first work to tackle the problem of effective email subject line generation.

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Proceedings of the Third Workshop on Abusive Language Online
Sarah T. Roberts | Joel Tetreault | Vinodkumar Prabhakaran | Zeerak Waseem
Proceedings of the Third Workshop on Abusive Language Online

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The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction
Phu Mon Htut | Joel Tetreault
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

In this paper, we investigate the impact of using 4 recent neural models for generating artificial errors to help train the neural grammatical error correction models. We conduct a battery of experiments on the effect of data size, models, and comparison with a rule-based approach.

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Dialogue Act Classification with Context-Aware Self-Attention
Vipul Raheja | Joel Tetreault
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)

Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evaluations on standard Dialogue Act classification datasets and show significant improvement over state-of-the-art results on the Switchboard Dialogue Act (SwDA) Corpus. We also investigate the impact of different utterance-level representation learning methods and show that our method is effective at capturing utterance-level semantic text representations while maintaining high accuracy.

2018

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Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
Sudha Rao | Joel Tetreault
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic transfer (formality) and show that techniques from the machine translation community can serve as strong baselines for future work. We also discuss challenges of using automatic metrics.

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Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Ekaterina Kochmar | Claudia Leacock | Helen Yannakoudakis
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

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Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
Alice Lai | Joel Tetreault
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

To date there has been very little work on assessing discourse coherence methods on real-world data. To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. We show that neural models, including two that we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains.

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How do you correct run-on sentences it’s not as easy as it seems
Junchao Zheng | Courtney Napoles | Joel Tetreault | Kostiantyn Omelianchuk
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text

Run-on sentences are common grammatical mistakes but little research has tackled this problem to date. This work introduces two machine learning models to correct run-on sentences that outperform leading methods for related tasks, punctuation restoration and whole-sentence grammatical error correction. Due to the limited annotated data for this error, we experiment with artificially generating training data from clean newswire text. Our findings suggest artificial training data is viable for this task. We discuss implications for correcting run-ons and other types of mistakes that have low coverage in error-annotated corpora.

2017

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JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
Courtney Napoles | Keisuke Sakaguchi | Joel Tetreault
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for developing and evaluating grammatical error correction (GEC). Unlike other corpora, it represents a broad range of language proficiency levels and uses holistic fluency edits to not only correct grammatical errors but also make the original text more native sounding. We describe the types of corrections made and benchmark four leading GEC systems on this corpus, identifying specific areas in which they do well and how they can improve. JFLEG fulfills the need for a new gold standard to properly assess the current state of GEC.

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Finding Good Conversations Online: The Yahoo News Annotated Comments Corpus
Courtney Napoles | Joel Tetreault | Aasish Pappu | Enrica Rosato | Brian Provenzale
Proceedings of the 11th Linguistic Annotation Workshop

This work presents a dataset and annotation scheme for the new task of identifying “good” conversations that occur online, which we call ERICs: Engaging, Respectful, and/or Informative Conversations. We develop a taxonomy to reflect features of entire threads and individual comments which we believe contribute to identifying ERICs; code a novel dataset of Yahoo News comment threads (2.4k threads and 10k comments) and 1k threads from the Internet Argument Corpus; and analyze the features characteristic of ERICs. This is one of the largest annotated corpora of online human dialogues, with the most detailed set of annotations. It will be valuable for identifying ERICs and other aspects of argumentation, dialogue, and discourse.

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Proceedings of the First Workshop on Abusive Language Online
Zeerak Waseem | Wendy Hui Kyong Chung | Dirk Hovy | Joel Tetreault
Proceedings of the First Workshop on Abusive Language Online

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Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Claudia Leacock | Helen Yannakoudakis
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

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A Report on the 2017 Native Language Identification Shared Task
Shervin Malmasi | Keelan Evanini | Aoife Cahill | Joel Tetreault | Robert Pugh | Christopher Hamill | Diane Napolitano | Yao Qian
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is typically framed as a classification task where the set of L1s is known a priori. Two previous shared tasks on NLI have been organized where the aim was to identify the L1 of learners of English based on essays (2013) and spoken responses (2016) they provided during a standardized assessment of academic English proficiency. The 2017 shared task combines the inputs from the two prior tasks for the first time. There are three tracks: NLI on the essay only, NLI on the spoken response only (based on a transcription of the response and i-vector acoustic features), and NLI using both responses. We believe this makes for a more interesting shared task while building on the methods and results from the previous two shared tasks. In this paper, we report the results of the shared task. A total of 19 teams competed across the three different sub-tasks. The fusion track showed that combining the written and spoken responses provides a large boost in prediction accuracy. Multiple classifier systems (e.g. ensembles and meta-classifiers) were the most effective in all tasks, with most based on traditional classifiers (e.g. SVMs) with lexical/syntactic features.

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GEC into the future: Where are we going and how do we get there?
Keisuke Sakaguchi | Courtney Napoles | Joel Tetreault
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

The field of grammatical error correction (GEC) has made tremendous bounds in the last ten years, but new questions and obstacles are revealing themselves. In this position paper, we discuss the issues that need to be addressed and provide recommendations for the field to continue to make progress, and propose a new shared task. We invite suggestions and critiques from the audience to make the new shared task a community-driven venture.

2016

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Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest
Dragomir Radev | Amanda Stent | Joel Tetreault | Aasish Pappu | Aikaterini Iliakopoulou | Agustin Chanfreau | Paloma de Juan | Jordi Vallmitjana | Alejandro Jaimes | Rahul Jha | Robert Mankoff
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.

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There’s No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction
Courtney Napoles | Keisuke Sakaguchi | Joel Tetreault
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Claudia Leacock | Helen Yannakoudakis
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

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Do Characters Abuse More Than Words?
Yashar Mehdad | Joel Tetreault
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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An Empirical Analysis of Formality in Online Communication
Ellie Pavlick | Joel Tetreault
Transactions of the Association for Computational Linguistics, Volume 4

This paper presents an empirical study of linguistic formality. We perform an analysis of humans’ perceptions of formality in four different genres. These findings are used to develop a statistical model for predicting formality, which is evaluated under different feature settings and genres. We apply our model to an investigation of formality in online discussion forums, and present findings consistent with theories of formality and linguistic coordination.

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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.

2015

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Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Claudia Leacock
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

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Oracle and Human Baselines for Native Language Identification
Shervin Malmasi | Joel Tetreault | Mark Dras
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

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It Depends: Dependency Parser Comparison Using A Web-based Evaluation Tool
Jinho D. Choi | Joel Tetreault | Amanda Stent
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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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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Predicting Grammaticality on an Ordinal Scale
Michael Heilman | Aoife Cahill | Nitin Madnani | Melissa Lopez | Matthew Mulholland | Joel Tetreault
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Non-Monotonic Parsing of Fluent Umm I mean Disfluent Sentences
Mohammad Sadegh Rasooli | Joel Tetreault
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

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Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Claudia Leacock
Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

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Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages
Yoav Goldberg | Yuval Marton | Ines Rehbein | Yannick Versley | Özlem Çetinoğlu | Joel Tetreault
Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages

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Automated Grammatical Error Correction for Language Learners
Joel Tetreault | Claudia Leacock
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Tutorial Abstracts

2013

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Joint Parsing and Disfluency Detection in Linear Time
Mohammad Sadegh Rasooli | Joel Tetreault
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the First Workshop on Metaphor in NLP
Ekaterina Shutova | Beata Beigman Klebanov | Joel Tetreault | Zornitsa Kozareva
Proceedings of the First Workshop on Metaphor in NLP

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Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Claudia Leacock
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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A Report on the First Native Language Identification Shared Task
Joel Tetreault | Daniel Blanchard | Aoife Cahill
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task
Hwee Tou Ng | Joel Tetreault | Siew Mei Wu | Yuanbin Wu | Christian Hadiwinoto
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task

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The CoNLL-2013 Shared Task on Grammatical Error Correction
Hwee Tou Ng | Siew Mei Wu | Yuanbin Wu | Christian Hadiwinoto | Joel Tetreault
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task

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Robust Systems for Preposition Error Correction Using Wikipedia Revisions
Aoife Cahill | Nitin Madnani | Joel Tetreault | Diane Napolitano
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Identifying High-Level Organizational Elements in Argumentative Discourse
Nitin Madnani | Michael Heilman | Joel Tetreault | Martin Chodorow
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Re-examining Machine Translation Metrics for Paraphrase Identification
Nitin Madnani | Joel Tetreault | Martin Chodorow
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Correcting Comma Errors in Learner Essays, and Restoring Commas in Newswire Text
Ross Israel | Joel Tetreault | Martin Chodorow
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Joel Tetreault | Jill Burstein | Claudia Leacock
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Exploring Grammatical Error Correction with Not-So-Crummy Machine Translation
Nitin Madnani | Joel Tetreault | Martin Chodorow
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Precision Isn’t Everything: A Hybrid Approach to Grammatical Error Detection
Michael Heilman | Aoife Cahill | Joel Tetreault
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Problems in Evaluating Grammatical Error Detection Systems
Martin Chodorow | Markus Dickinson | Ross Israel | Joel Tetreault
Proceedings of COLING 2012

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Native Tongues, Lost and Found: Resources and Empirical Evaluations in Native Language Identification
Joel Tetreault | Daniel Blanchard | Aoife Cahill | Martin Chodorow
Proceedings of COLING 2012

2011

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Exploiting Syntactic and Distributional Information for Spelling Correction with Web-Scale N-gram Models
Wei Xu | Joel Tetreault | Martin Chodorow | Ralph Grishman | Le Zhao
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Claudia Leacock
Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications

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E-rating Machine Translation
Kristen Parton | Joel Tetreault | Nitin Madnani | Martin Chodorow
Proceedings of the Sixth Workshop on Statistical Machine Translation

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They Can Help: Using Crowdsourcing to Improve the Evaluation of Grammatical Error Detection Systems
Nitin Madnani | Martin Chodorow | Joel Tetreault | Alla Rozovskaya
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Using Entity-Based Features to Model Coherence in Student Essays
Jill Burstein | Joel Tetreault | Slava Andreyev
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Claudia Leacock
Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications

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Rethinking Grammatical Error Annotation and Evaluation with the Amazon Mechanical Turk
Joel Tetreault | Elena Filatova | Martin Chodorow
Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications

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Towards Using Structural Events To Assess Non-native Speech
Lei Chen | Joel Tetreault | Xiaoming Xi
Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications

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Using Parse Features for Preposition Selection and Error Detection
Joel Tetreault | Jennifer Foster | Martin Chodorow
Proceedings of the ACL 2010 Conference Short Papers

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Using an Error-Annotated Learner Corpus to Develop an ESL/EFL Error Correction System
Na-Rae Han | Joel Tetreault | Soo-Hwa Lee | Jin-Young Ha
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper presents research on building a model of grammatical error correction, for preposition errors in particular, in English text produced by language learners. Unlike most previous work which trains a statistical classifier exclusively on well-formed text written by native speakers, we train a classifier on a large-scale, error-tagged corpus of English essays written by ESL learners, relying on contextual and grammatical features surrounding preposition usage. First, we show that such a model can achieve high performance values: 93.3% precision and 14.8% recall for error detection and 81.7% precision and 13.2% recall for error detection and correction when tested on preposition replacement errors. Second, we show that this model outperforms models trained on well-edited text produced by native speakers of English. We discuss the implications of our approach in the area of language error modeling and the issues stemming from working with a noisy data set whose error annotations are not exhaustive.

2009

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Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Claudia Leacock
Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications

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Human Evaluation of Article and Noun Number Usage: Influences of Context and Construction Variability
John Lee | Joel Tetreault | Martin Chodorow
Proceedings of the Third Linguistic Annotation Workshop (LAW III)

2008

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Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
Joel Tetreault | Jill Burstein | Rachele De Felice
Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications

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Native Judgments of Non-Native Usage: Experiments in Preposition Error Detection
Joel Tetreault | Martin Chodorow
Coling 2008: Proceedings of the workshop on Human Judgements in Computational Linguistics

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The Ups and Downs of Preposition Error Detection in ESL Writing
Joel R. Tetreault | Martin Chodorow
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Estimating the Reliability of MDP Policies: a Confidence Interval Approach
Joel Tetreault | Dan Bohus | Diane Litman
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Comparing User Simulation Models For Dialog Strategy Learning
Hua Ai | Joel Tetreault | Diane Litman
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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Exploring Affect-Context Dependencies for Adaptive System Development
Kate Forbes-Riley | Mihai Rotaru | Diane Litman | Joel Tetreault
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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Detection of Grammatical Errors Involving Prepositions
Martin Chodorow | Joel Tetreault | Na-Rae Han
Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions

2006

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Using Reinforcement Learning to Build a Better Model of Dialogue State
Joel R. Tetreault | Diane J. Litman
11th Conference of the European Chapter of the Association for Computational Linguistics

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Comparing the Utility of State Features in Spoken Dialogue Using Reinforcement Learning
Joel Tetreault | Diane Litman
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2004

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Semi-automatic Syntactic and Semantic Corpus Annotation with a Deep Parser
Mary D. Swift | Myroslava O. Dzikovska | Joel R. Tetreault | James F. Allen
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Evaluation of Transcription and Annotation Tools for a Multi-modal, Multi-party Dialogue Corpus
Saurabh Garg | Bilyana Martinovski | Susan Robinson | Jens Stephan | Joel Tetreault | David R. Traum
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Discourse Annotation in the Monroe Corpus
Joel Tetreault | Mary Swift | Preethum Prithviraj | Myroslava Dzikovska | James Allen
Proceedings of the Workshop on Discourse Annotation

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Incremental Parsing with Reference Interaction
Scott C. Stoness | Joel Tetreault | James Allen
Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together

2001

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A Corpus-Based Evaluation of Centering and Pronoun Resolution
Joel R. Tetreault
Computational Linguistics, Volume 27, Number 4, December 2001

1999

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A Flexible Architecture for Reference Resolution
Donna K. Byron | Joel R. Tetreault
Ninth Conference of the European Chapter of the Association for Computational Linguistics

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Analysis of Syntax-Based Pronoun Resolution Methods
Joel R. Tetreault
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

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