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2018 | OriginalPaper | Buchkapitel

Chinese Grammatical Error Correction Using Statistical and Neural Models

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

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel LM Enhanced BiRNN-CRF for Joint Chinese Word Segmentation and POS Tagging

Nächstes Kapitel Multi-turn Inference Matching Network for Natural Language Inference

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Titel: Chinese Grammatical Error Correction Using Statistical and Neural Models
verfasst von: Junpei Zhou
Chen Li
Hengyou Liu
Zuyi Bao
Guangwei Xu
Linlin Li
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-319-99500-7

Electronic ISBN: 978-3-319-99501-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-99501-4_10

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