Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings

Abstract

In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns. Most existing algorithms for learning word embeddings usually model only the syntactic context of words and do not consider grammatical errors specific to language learners. Therefore, we propose methods to learn word embeddings specialized for grammatical errors by considering grammaticality and grammatical error patterns. We determine grammaticality of n-gram sequence from the annotated error tags and extract grammatical error patterns for word embeddings from large-scale learner corpora. Experimental results show that a bidirectional long-short term memory model initialized by our word embeddings achieved the state-of-the-art accuracy by a large margin in an English grammatical error detection task on the First Certificate in English dataset.