Dekang Lin


2018

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A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension
Jiahua Liu | Wan Wei | Maosong Sun | Hao Chen | Yantao Du | Dekang Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The task of machine reading comprehension (MRC) has evolved from answering simple questions from well-edited text to answering real questions from users out of web data. In the real-world setting, full-body text from multiple relevant documents in the top search results are provided as context for questions from user queries, including not only questions with a single, short, and factual answer, but also questions about reasons, procedures, and opinions. In this case, multiple answers could be equally valid for a single question and each answer may occur multiple times in the context, which should be taken into consideration when we build MRC system. We propose a multi-answer multi-task framework, in which different loss functions are used for multiple reference answers. Minimum Risk Training is applied to solve the multi-occurrence problem of a single answer. Combined with a simple heuristic passage extraction strategy for overlong documents, our model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09.

2014

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Transactions of the Association for Computational Linguistics, Volume 2
Dekang Lin | Michael Collins | Lillian Lee
Transactions of the Association for Computational Linguistics, Volume 2

2013

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Transactions of the Association for Computational Linguistics, Volume 1
Dekang Lin | Michael Collins
Transactions of the Association for Computational Linguistics, Volume 1

2012

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Unsupervised Translation Sense Clustering
Mohit Bansal | John DeNero | Dekang Lin
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Dekang Lin | Yuji Matsumoto | Rada Mihalcea
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Dekang Lin | Yuji Matsumoto | Rada Mihalcea
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Using Web-scale N-grams to Improve Base NP Parsing Performance
Emily Pitler | Shane Bergsma | Dekang Lin | Kenneth Church
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Creating Robust Supervised Classifiers via Web-Scale N-Gram Data
Shane Bergsma | Emily Pitler | Dekang Lin
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Proceedings of the NAACL HLT 2010 Sixth Web as Corpus Workshop
Adam Kilgarriff | Dekang Lin
Proceedings of the NAACL HLT 2010 Sixth Web as Corpus Workshop

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Improved Natural Language Learning via Variance-Regularization Support Vector Machines
Shane Bergsma | Dekang Lin | Dale Schuurmans
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

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New Tools for Web-Scale N-grams
Dekang Lin | Kenneth Church | Heng Ji | Satoshi Sekine | David Yarowsky | Shane Bergsma | Kailash Patil | Emily Pitler | Rachel Lathbury | Vikram Rao | Kapil Dalwani | Sushant Narsale
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

While the web provides a fantastic linguistic resource, collecting and processing data at web-scale is beyond the reach of most academic laboratories. Previous research has relied on search engines to collect online information, but this is hopelessly inefficient for building large-scale linguistic resources, such as lists of named-entity types or clusters of distributionally similar words. An alternative to processing web-scale text directly is to use the information provided in an N-gram corpus. An N-gram corpus is an efficient compression of large amounts of text. An N-gram corpus states how often each sequence of words (up to length N) occurs. We propose tools for working with enhanced web-scale N-gram corpora that include richer levels of source annotation, such as part-of-speech tags. We describe a new set of search tools that make use of these tags, and collectively lower the barrier for lexical learning and ambiguity resolution at web-scale. They will allow novel sources of information to be applied to long-standing natural language challenges.

2009

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Phrase Clustering for Discriminative Learning
Dekang Lin | Xiaoyun Wu
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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Gender and Animacy Knowledge Discovery from Web-Scale N-Grams for Unsupervised Person Mention Detection
Heng Ji | Dekang Lin
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

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Flexible Answer Typing with Discriminative Preference Ranking
Christopher Pinchak | Dekang Lin | Davood Rafiei
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Glen, Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender
Shane Bergsma | Dekang Lin | Randy Goebel
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

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Proceedings of the NAACL HLT 2009 Workshop on Semi-supervised Learning for Natural Language Processing
Qin Iris Wang | Kevin Duh | Dekang Lin
Proceedings of the NAACL HLT 2009 Workshop on Semi-supervised Learning for Natural Language Processing

2008

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Distributional Identification of Non-Referential Pronouns
Shane Bergsma | Dekang Lin | Randy Goebel
Proceedings of ACL-08: HLT

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Semi-Supervised Convex Training for Dependency Parsing
Qin Iris Wang | Dale Schuurmans | Dekang Lin
Proceedings of ACL-08: HLT

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Mining Parenthetical Translations from the Web by Word Alignment
Dekang Lin | Shaojun Zhao | Benjamin Van Durme | Marius Paşca
Proceedings of ACL-08: HLT

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Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Johan Bos | Edward Briscoe | Aoife Cahill | John Carroll | Stephen Clark | Ann Copestake | Dan Flickinger | Josef van Genabith | Julia Hockenmaier | Aravind Joshi | Ronald Kaplan | Tracy Holloway King | Sandra Kuebler | Dekang Lin | Jan Tore Lønning | Christopher Manning | Yusuke Miyao | Joakim Nivre | Stephan Oepen | Kenji Sagae | Nianwen Xue | Yi Zhang
Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation

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Discriminative Learning of Selectional Preference from Unlabeled Text
Shane Bergsma | Dekang Lin | Randy Goebel
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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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Bootstrapping Path-Based Pronoun Resolution
Shane Bergsma | Dekang Lin
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Names and Similarities on the Web: Fact Extraction in the Fast Lane
Marius Paşca | Dekang Lin | Jeffrey Bigham | Andrei Lifchits | Alpa Jain
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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

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A Probabilistic Answer Type Model
Christopher Pinchak | Dekang Lin
11th Conference of the European Chapter of the Association for Computational Linguistics

2005

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Strictly Lexical Dependency Parsing
Qin Iris Wang | Dale Schuurmans | Dekang Lin
Proceedings of the Ninth International Workshop on Parsing Technology

2004

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A Path-based Transfer Model for Machine Translation
Dekang Lin
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing
Dekang Lin | Dekai Wu
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

2003

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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

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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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Automatically Discovering Word Senses
Patrick Pantel | Dekang Lin
Companion Volume of the Proceedings of HLT-NAACL 2003 - Demonstrations

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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

2002

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Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics
Pierre Isabelle | Eugene Charniak | Dekang Lin
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Concept Discovery from Text
Dekang Lin | Patrick Pantel
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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LaTaT: Language and Text Analysis Tools
Dekang Lin
Proceedings of the First International Conference on Human Language Technology Research

2000

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Chinese-English Semantic Resource Construction
Bonnie J. Dorr | Gina-Anne Levow | Dekang Lin | Scott Thomas
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Word-for-Word Glossing with Contextually Similar Words
Patrick Pantel | Dekang Lin
1st Meeting of the North American Chapter of the Association for Computational Linguistics

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Building a Chinese-English mapping between verb concepts for multilingual applications
Bonnie J. Dorr | Gina-Anne Levow | Dekang Lin
Proceedings of the Fourth Conference of the Association for Machine Translation in the Americas: Technical Papers

This paper addresses the problem of building conceptual resources for multilingual applications. We describe new techniques for large-scale construction of a Chinese-English lexicon for verbs, using thematic-role information to create links between Chinese and English conceptual information. We then present an approach to compensating for gaps in the existing resources. The resulting lexicon is used for multilingual applications such as machine translation and cross-language information retrieval.

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An Unsupervised Approach to Prepositional Phrase Attachment using Contextually Similar Words
Patrick Pantel | Dekang Lin
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

1999

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Book Reviews: WordNet: An Electronic Lexical Database
Dekang Lin
Computational Linguistics, Volume 25, Number 2, June 1999

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Automatic Identification of Non-compositional Phrases
Dekang Lin
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

1998

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Automatic Retrieval and Clustering of Similar Words
Dekang Lin
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Using Collocation Statistics in Information Extraction
Dekang Lin
Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, April 29 - May 1, 1998

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Automatic Retrieval and Clustering of Similar Words
Dekang Lin
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

1997

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Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity
Dekang Lin
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

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A Broad-Coverage Word Sense Tagger
Dekang Lin
Fifth Conference on Applied Natural Language Processing: Descriptions of System Demonstrations and Videos

1996

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On the Structural Complexity of Natural Language Sentences
Dekang Lin
COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics

1995

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University of Manitoba: Description of the PIE System Used for MUC-6
Dekang Lin
Sixth Message Understanding Conference (MUC-6): Proceedings of a Conference Held in Columbia, Maryland, November 6-8, 1995

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Squibs and Discussions: Efficient Parsing for Korean and English: A Parameterized Message-Passing Approach
Bonnie J. Dorr | Jye-hoon Lee | Dekang Lin | Sungki Suh
Computational Linguistics, Volume 21, Number 2, June 1995

1994

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A Parameter-Based Message-Passing Parser for MT of Korean and English
Dekang Lin | Bonnie Dorr | Jye-hoon Lee | Sungki Suh
Proceedings of the First Conference of the Association for Machine Translation in the Americas

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PRINCIPAR - An Efficient, Broad-coverage, Principle-based Parser
Dekang Lin
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics

1993

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University of Manitoba: Description of the NUBA System as Used for MUC-5
Dekang Lin
Fifth Message Understanding Conference (MUC-5): Proceedings of a Conference Held in Baltimore, Maryland, August 25-27, 1993

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Principle-Based Parsing Without Overgeneration
Dekang Lin
31st Annual Meeting of the Association for Computational Linguistics