Abstract

Recent works in Grammatical Error Correction (GEC) have leveraged the progress in Neural Machine Translation (NMT), to learn rewrites from parallel corpora of grammatically incorrect and corrected sentences, achieving state-of-the-art results. At the same time, Generative Adversarial Networks (GANs) have been successful in generating realistic texts across many different tasks by learning to directly minimize the difference between human-generated and synthetic text. In this work, we present an adversarial learning approach to GEC, using the generator-discriminator framework. The generator is a Transformer model, trained to produce grammatically correct sentences given grammatically incorrect ones. The discriminator is a sentence-pair classification model, trained to judge a given pair of grammatically incorrect-correct sentences on the quality of grammatical correction. We pre-train both the discriminator and the generator on parallel texts and then fine-tune them further using a policy gradient method that assigns high rewards to sentences which could be true corrections of the grammatically incorrect text. Experimental results on FCE, CoNLL-14, and BEA-19 datasets show that Adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines.

Highlights

  • Grammatical Error Correction (GEC) has grown into a popular NLP task that deals with building systems for automatically correcting errors in written text (Ng et al, 2013, 2014)

  • Experimental results indicate that our model can achieve significantly better performance than strong Neural Machine Translation (NMT)-based baselines

  • We propose a task-appropriate training objective for GEC, using an adversarial training framework consisting of a generator and a discriminator, based on the Adversarial-NMT framework of Wu et al (2018)

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Summary

Introduction

Grammatical Error Correction (GEC) has grown into a popular NLP task that deals with building systems for automatically correcting errors in written text (Ng et al, 2013, 2014). Several Neural Machine Translation (NMT) systems have been developed with promising results (Sutskever et al, 2014; Bahdanau et al, 2015; Cho et al, 2014), and their successful application to GEC, either in combination with SMT models (Chollampatt et al, 2016; Yuan and Briscoe, 2016; Yannakoudakis et al, 2017; Grundkiewicz and Junczys-Dowmunt, 2018), or strictly as neural models, has emerged as the new state-of-theart (Xie et al, 2016; Schmaltz et al, 2017; Sakaguchi et al, 2017; Ji et al, 2017; Ge et al, 2018; Junczys-Dowmunt et al, 2018; Chollampatt and Ng, 2018a,b; Zhao et al, 2019). In order to avoid these issues, we take a different approach, inspired by Generative Adversarial Networks (GANs) (Goodfellow et al, 2014), which

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