Automated Grammatical Error Correction for Language Learners

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 writers 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 had 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., 2010; Leacock et al., 2014). In 2011 and 2012, the first shared tasks in GEC (Dale and Kilgarriff, 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; Ng et al., 2014). 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: