This paper describes the submission of the National University of Singapore (NUS) to the Helping Our Own (HOO) Pilot Shared Task. Our system targets spelling, article, and preposition errors in a sequential processing pipeline. 1

We present a novel approach for automatic collocation error correction in learner English which is based on paraphrases extracted from parallel corpora. Our key assumption is that collocation errors are often caused by semantic similarity in the first language (L1language) of the writer. An analysis of a large corpus of annotated learner English confirms this assumption. We evaluate our approach on real-world learner data and show that L1-induced paraphrases outperform traditional approaches based on edit distance, homophones, and WordNet synonyms. 1

We present a novel beam-search decoder for grammatical error correction. The decoder iteratively generates new hypothesis corrections from current hypotheses and scores them based on features of grammatical correctness and fluency. These features include scores from discriminative classifiers for specific error categories, such as articles and prepositions. Unlike all previous approaches, our method is able to perform correction of whole sentences with multiple and interacting errors while still taking advantage of powerful existing classifier approaches. Our decoder achieves an F1 correction score significantly higher than all previous published scores on the Helping Our Own (HOO) shared task data set. 1