Paper: Bilingual Random Walk Models for Automated Grammar Correction of ESL Author-Produced Text

ACL ID W11-1421
Title Bilingual Random Walk Models for Automated Grammar Correction of ESL Author-Produced Text
Venue Innovative Use of NLP for Building Educational Applications
Session
Year 2011
Authors

We present a novel noisy channel model for correcting text produced by English as a sec- ond language (ESL) authors. We model the English word choices made by ESL authors as a random walk across an undirected bipartite dictionary graph composed of edges between English words and associated words in an au- thor’s native language. We present two such models, using cascades of weighted finite- state transducers (wFSTs) to model language model priors, random walk-induced noise, and observed sentences, and expectation maxi- mization (EM) to learn model parameters af- ter Park and Levy (2011). We show that such models can make intelligent word substitu- tions to improve grammaticality in an unsu- pervised setting.