Paper: Algorithm Selection and Model Adaptation for ESL Correction Tasks

ACL ID P11-1093
Title Algorithm Selection and Model Adaptation for ESL Correction Tasks
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

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, ...