NDLI: Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Binbin Chen Jingyu Zhang
Abstract	Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops. The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction. Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns. Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones. Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors. The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students’ sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores. The pre-training can enhance the restoring efficiency of sentence structure in the decoding process. The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students’ grammatical error correction or in a common grammatical error correction shared task.
e-ISSN	21693536
DOI	10.1109/ACCESS.2022.3159676
Journal	IEEE Access
Volume Number	10
Language	English
Publisher	IEEE
Publisher Date	2022-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Hearing Impaired Student Grammatical Error Correction Self-attention Encoder-decoder Pre-training
Content Type	Text
Resource Type	Article

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