Abstract

The long-term goal of our work is to develop a system which detects errors in grammar and usage so that appropriate feedback can be given to non-native English writers, a large and growing segment of the world’s population. Estimates are that in China alone as many as 300 million people are currently studying English as a second language (ESL). In particular, usage errors involving prepositions are among the most common types seen in the writing of non-native English speakers. For example, Izumi et al., (2003) reported error rates for English prepositions that were as high as 10% in a Japanese learner corpus.

Since prepositions are such a nettlesome problem for ESL writers, developing an NLP application that can reliably detect these types of errors will provide an invaluable learning resource to ESL students. In this talk we first review one popular machine learning methodology for detecting preposition and article errors in texts written by ESL writers. Next, we describe a novel approach to ESL grammatical error detection: using round-trip machine translation to automatically correct errors.

This is joint work with Nitin Madnani (ETS) and Martin Chodorow (CUNY).

Speaker Bio:

Joel Tetreault is a Managing Senior Research Scientist specializing in Computational Linguistics in the Research & Development Division at Educational Testing Service in Princeton, NJ. His research focus is Natural Language Processing with specific interests in anaphora, dialogue and discourse processing, machine learning, and applying these techniques to the analysis of English language learning and automated essay scoring. Currently he is working on automated methods for detecting grammatical errors by non-native speakers, plagiarism detection, and content scoring methods. Previously, he was a postdoctoral research scientist at the University of Pittsburgh’s Learning Research and Development Center (2004-2007). There he worked on developing spoken dialogue tutoring systems. Tetreault received his B.A. in Computer Science from Harvard University (1998) and his M.S. and Ph.D. in Computer Science from the University of Rochester (2004).