A Nested Attention Neural Hybrid Model for Grammatical Error Correction

Abstract

Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information,and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography.

References in corpus (2)

• Neural Language Correction with Character-Based Attention
• Neural Network Translation Models for Grammatical Error Correction

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• An Introductory Survey on Attention Mechanisms in NLP Problems
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• A Simple but Effective Classification Model for Grammatical Error Correction
• Paying Attention to Attention: Highlighting Influential Samples in Sequential Analysis
• Learning Multi-level Dependencies for Robust Word Recognition
• KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction
• Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models