Chinese Grammatical Error Correction Using Statistical and Neural Models

Junpei Zhou, Chen Li, Hengyou Liu, Zuyi Bao, Guangwei Xu, Linlin Li

Abstract: This paper introduces the Alibaba NLP team’s system for NLPCC 2018 shared task of Chinese Grammatical Error Correction (GEC). Chinese as a Second Language (CSL) learners can use this system to correct grammatical errors in texts they wrote. We proposed a method to combine statistical and neural models for the GEC task. This method consists of two modules: the correction module and the combination module. In the correction module, two statistical models and one neural model generate correction candidates for each input sentence. Those two statistical models are a rule-based model and a statistical machine translation (SMT)-based model. The neural model is a neural machine translation (NMT)-based model. In the combination module, we implemented it in a hierarchical manner. We first combined models at a lower level, which means we trained several models with different configurations and combined them. Then we combined those two statistical models and a neural model at the higher level. Our system reached the second place on the leaderboard released by the official.