Improving Grammatical Error Correction with Machine Translation Pairs

By Wangchunshu Zhou, Tao Ge, Chang Mu, Ke Xu, Furu Wei, M. Zhou. 2019

43 citations recorded by the source corpus.

We propose a novel data synthesis method to generate diverse error-corrected sentence pairs for improving grammatical error correction, which is based on a pair of machine translation models (e.g., Chinese to English) of different qualities (i.e., poor and good). The poor translation model can resemble the ESL (English as a second language) learner and tends to generate translations of low quality in terms of fluency and grammaticality, while the good translation model generally generates fluent and grammatically correct translations. With the pair of translation models, we can generate unlimited numbers of poor to good English sentence pairs from text in the source language (e.g., Chinese) of the translators. Our approach can generate various error-corrected patterns and nicely complement the other data synthesis approaches for GEC. Experimental results demonstrate the data generated by our approach can effectively help a GEC model to improve the performance and achieve the state-of-the-art single-model performance in BEA-19 and CoNLL-14 benchmark datasets.

Subfields: Artificial Intelligence, Natural Language Processing, Machine Translation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tao Ge
2. Furu Wei
3. Xin Sun
4. Houfeng Wang
5. Li Dong
6. Yoav Goldberg
7. Jianfeng Gao
8. Xipeng Qiu

In Natural Language Processing

1. Tao Ge
2. Furu Wei
3. Xin Sun
4. Houfeng Wang
5. Ming Zhou
6. M. Zhou
7. Li Dong
8. Yang Liu

In Machine Translation

1. Xin Sun
2. Houfeng Wang
3. Furu Wei
4. Tao Ge

Citations in the corpus

• Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding (2021, background): “2019; Lichtarge et al., 2019; Zhou et al., 2020; Wan et al., 2020), most methods that improve performance (Ge et al., 2018b; Kaneko et al., 2020) introduce additional computational cost and thus slow down inference despite the performance improvement.”
• Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction (2020, unknown): “However, except those adding synthetic erroneous data (Xie et al., 2018; Ge et al., 2018a; Grundkiewicz et al., 2019; Kiyono et al., 2019; Zhou et al., 2019) and Wikipedia revision logs (Lichtarge et al., 2019) for training, most methods cause an increase in latency.”