Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation

• URL: http://arxiv.org/abs/2205.10884v1
• Date: Sun, 22 May 2022 17:47:06 GMT
• Title: Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation
• Authors: Jiquan Li, Junliang Guo, Yongxin Zhu, Xin Sheng, Deqiang Jiang, Bo Ren, Linli Xu
• Abstract summary: We propose a novel Sequence-to-Action(S2A) module for Grammatical Error Correction. The S2A module jointly takes the source and target sentences as input, and is able to automatically generate a token-level action sequence. Our model consistently outperforms the seq2seq baselines, while being able to significantly alleviate the over-correction problem.
• Score: 21.886973310718457
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years. While one of the key principles of GEC is to keep the correct parts unchanged and avoid over-correction, previous sequence-to-sequence (seq2seq) models generate results from scratch, which are not guaranteed to follow the original sentence structure and may suffer from the over-correction problem. In the meantime, the recently proposed sequence tagging models can overcome the over-correction problem by only generating edit operations, but are conditioned on human designed language-specific tagging labels. In this paper, we combine the pros and alleviate the cons of both models by proposing a novel Sequence-to-Action~(S2A) module. The S2A module jointly takes the source and target sentences as input, and is able to automatically generate a token-level action sequence before predicting each token, where each action is generated from three choices named SKIP, COPY and GENerate. Then the actions are fused with the basic seq2seq framework to provide final predictions. We conduct experiments on the benchmark datasets of both English and Chinese GEC tasks. Our model consistently outperforms the seq2seq baselines, while being able to significantly alleviate the over-correction problem as well as holding better generality and diversity in the generation results compared to the sequence tagging models.

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