Martin Chodorow, Michael Gamon, and Joel Tetreault
In this paper, we describe and evaluate two state-of-the-art systems for identifying and correcting writing errors involving English articles and prepositions. CriterionSM, developed by Educational Testing Service, and ESL Assistant, developed by Microsoft Research, both use machine learning techniques to build models of article and preposition usage which enable them to identify errors and suggest corrections to the writer. We evaluated the effects of these systems on users in two studies. In one, Criterion provided feedback about article errors to native and non-native speakers who were writing an essay for a college-level psychology course. The results showed a significant reduction in the number of article errors in the final essays of the non-native speakers. In the second study, ESL Assistant was used by non-native speakers who were composing email messages. The results indicated that users were selective in their choices among the system’s suggested corrections and that, as a result, they were able to increase the proportion of valid corrections by making effective use of feedback.
In Language Testing