[Elsnet-list] COLING 2014 Tutorial on Automated Grammatical Error Correction for Language Learners

Joel Tetreault tetreaul at gmail.com
Thu Jun 5 17:05:02 CEST 2014

(apologies for cross-posting and any inadvertent grammatical errors)

*COLING 2014 Tutorial on Automated Grammatical Error Correction for
Language Learners*
Sunday, August 24, 2014
Dublin, Ireland


A fast growing area in Natural Language Processing is the use of automated
tools for identifying and correcting grammatical errors made by language
learners.  This growth, in part, has been fueled by the needs of a large
number of people in the world who are learning and using a second or
foreign language. For example, it is estimated that there are currently
over one billion people who are non-native speakers of English.  These
numbers drive the demand for accurate tools that can help learners to write
and speak proficiently in another language. Such demand also makes this an
exciting time for those in the NLP community who are developing automated
methods for grammatical error correction (GEC). Our motivation for the
COLING tutorial is to make others more aware of this field and its
particular set of challenges. For these reasons, we believe that the
tutorial will potentially benefit a broad range of conference attendees.

In general, there has been a surge in interest in using NLP to address
educational needs, which, in turn, has spawned the recurring ACL/NAACL
workshop “Innovative Use of Natural Language Processing for Building
Educational Applications” that will have its 9th edition at ACL 2014.  The
last three years, in particular, have been pivotal for GEC.  Papers on the
topic have become more commonplace at main conferences such as ACL, NAACL
and EMNLP, as well as two editions of a Morgan Claypool Synthesis Series
book on the topic (Leacock et al., 2014).  In 2011 and 2012, the first
shared tasks in GEC (Dale et al., 2011; Dale et al., 2012) were created,
and dozens of teams from all over the world participated.  This was
followed by two successful CoNLL Shared Tasks on the topic in 2013 and 2014
(Ng et al., 2013).

While there have been many exciting developments in GEC over the last few
years, there is still considerable room for improvement as state-of-the-art
performance in detecting and correcting several important error types is
still inadequate for real world applications.  We hope to engage
researchers from other NLP fields to develop novel and effective approaches
to these problems. Our tutorial is specifically designed to:

* Introduce an NLP audience to the challenges that language learners face
and thus the challenges of designing NLP tools to assist in language

* Provide a history of GEC and the state-of-the-art approaches for
different error types

* Show the need for cross-lingual error correction approaches and discuss
novel methods for achieving this

* Discuss ways in which error correction techniques can have an impact on
other NLP tasks


Claudia Leacock, Martin Chodorow, Michael Gamon and Joel Tetreault.  2014.
Automated Grammatical Error Detection for Language Learners, Second
Edition.  Synthesis Lectures on Human Language Technologies.  Morgan &
Claypool Publishers.

Robert Dale and Adam Kilgarriff. Helping Our Own: The HOO 2011 pilot shared
task. In Proceedings of the 13th European Workshop on Natural Language
Generation.  Nancy, France, September 2011.

Robert Dale, Ilya Anisimoff and George Narroway. HOO 2012: A report on the
preposition and determiner error correction shared task. In Proceedings of
the Seventh Workshop on Building Educational Applications Using NLP.
 Montreal, Canada, June 2012

Hwee Tou Ng, Siew Mei Wu, Yuanbin Wu, and Joel Tetreault. The Conll-2013
shared task on grammatical error correction. In Proceedings of the
Seventeenth Conference on Computational Natural Language Learning, Sofia,
Bulgaria, August 2013.


1. Introduction

2. Special Problems of Language Learners
* Errors made by English Language Learners (ELLs)
* Influence of L1

3. Heuristic rule-based approaches

4. Data Driven Approaches to Error Correction
* Methods for detection and correction
* Types of training data
* Features
* Web-based methods

5. Annotation and Evaluation
* Annotation schemes
* Proposals for efficient annotation
* Evaluation Measures
* Crowdsourcing for annotation and evaluation

6. Current Trends in Error Correction
* Detection of ungrammatical sentences and Other error types
* Shared tasks
* Going beyond the classification methodology
* Error correction in other languages

7. Conclusions


*Joel Tetreault* is a Senior Research Scientist at Yahoo! Labs in New York
City.  His research focus is Natural Language Processing with specific
interests in anaphora, dialogue and discourse processing, machine learning,
and applying these techniques to the analysis of English language learning
and automated essay scoring.  Previously he was Principal Manager of the
Core Natural Language group at Nuance Communications, Inc. where he worked
on the research and development of NLP tools and components for the next
generation of intelligent dialogue systems.  Prior to Nuance, he worked at
Educational Testing Service for six years as a Managing Senior Research
Scientist where he researched automated methods for detecting grammatical
errors by non-native speakers, plagiarism detection, and content scoring.
 Tetreault received his B.A. in Computer Science from Harvard University
(1998) and his M.S. and Ph.D. in Computer Science from the University of
Rochester (2004).  He was also a postdoctoral research scientist at the
University of Pittsburgh's Learning Research and Development Center
(2004-2007), where he worked on developing spoken dialogue tutoring
systems.  In addition he has co-organized the Building Educational
Application workshop series for 7 years, the CoNLL 2013 Shared Task on
Grammatical Error Correction, and is currently NAACL Treasurer.

*Claudia Leacock* is a Research Scientist at CTB McGraw-Hill who has been
working on using NLP in educational applications for 20 years focusing on
automated scoring and grammatical error detection. She was previously a
consultant for Microsoft Research where she collaborated on the development
of ESL Assistant: a web-based prototype tool for detecting and correcting
grammatical errors of English language learners. As a Distinguished Member
of Technical Staff at Pearson Knowledge Technologies, and previously as a
Principal Development Scientist at Educational Testing Service, she
developed tools for automated assessment of short-response content-based
questions and for grammatical error detection.  As a member of the WordNet
group at Princeton University’s Cognitive Science Lab, her research focused
on word sense disambiguation. Dr. Leacock received a B.A. in English from
NYU, a Ph.D. in Linguistics from the City University of New York, Graduate
Center and was a post-doctoral fellow at IBM, T.J. Watson Research Center.
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