Algorithm Selection and Model Adaptation for ESL Correction Tasks

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 924–933, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Algorithm Selection and Model Adaptation for ESL Correction Tasks Alla Rozovskaya and Dan Roth University of Illinois at Urbana-Champaign Urbana, IL 61801 {rozovska,danr}@illinois.edu Abstract We consider the problem of correcting errors made by English as a Second Language (ESL) writers and address two issues that are essen- tial to making progress in ESL error correction - algorithm selection and model adaptation to the first language of the ESL learner. A variety of learning algorithms have been applied to correct ESL mistakes, but often comparisons were made between incompara- ble data sets. We conduct an extensive, fair comparison of four popular learning methods for the task, reversing conclusions from ear- lier evaluations. Our results hold for different training sets, genres, and feature sets. A second key issue in ESL error correction is the adaptation of a model to the first lan- guage of the writer. Errors made by non-native speakers exhibit certain regularities and, as we show, models perform much better when they use knowledge about error patterns of the non- native writers. We propose a novel way to adapt a learned algorithm to the first language of the writer that is both cheaper to imple- ment and performs better than other adapta- tion methods. 1 Introduction There has been a lot of recent work on correct- ing writing mistakes made by English as a Second Language (ESL) learners (Izumi et al., 2003; Eeg- Olofsson and Knuttson, 2003; Han et al., 2006; Fe- lice and Pulman, 2008; Gamon et al., 2008; Tetreault and Chodorow, 2008; Elghaari et al., 2010; Tetreault et al., 2010; Gamon, 2010; Rozovskaya and Roth, 2010c). Most of this work has focused on correcting mistakes in article and preposition usage, which are some of the most common error types among non- native writers of English (Dalgish, 1985; Bitchener et al., 2005; Leacock et al., 2010). Examples below illustrate some of these errors: 1. “They listen to None*/the lecture carefully.” 2. “He is an engineer with a passion to*/for what he does.” In (1) the definite article is incorrectly omitted. In (2), the writer uses an incorrect preposition. Approaches to correcting preposition and article mistakes have adopted the methods of the context- sensitive spelling correction task, which addresses the problem of correcting spelling mistakes that re- sult in legitimate words, such as confusing their and there (Carlson et al., 2001; Golding and Roth, 1999). A candidate set or a confusion set is defined that specifies a list of confusable words, e.g., {their, there}. Each occurrence of a confusable word in text is represented as a vector of features derived from a context window around the target, e.g., words and part-of-speech tags. A classifier is trained on text assumed to be error-free. At decision time, for each word in text, e.g. there, the classifier predicts the most likely candidate from the corresponding con- fusion set {their, …

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