Spelling Error Correction with Soft-Masked BERT

• URL: http://arxiv.org/abs/2005.07421v1
• Date: Fri, 15 May 2020 09:02:38 GMT
• Title: Spelling Error Correction with Soft-Masked BERT
• Authors: Shaohua Zhang, Haoran Huang, Jicong Liu and Hang Li
• Abstract summary: A state-of-the-art method for the task selects a character from a list of candidates for correction at each position of the sentence on the basis of BERT. The accuracy of the method can be sub-optimal because BERT does not have sufficient capability to detect whether there is an error at each position. We propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT.
• Score: 11.122964733563117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-of-the-art method for the task selects a character from a list of candidates for correction (including non-correction) at each position of the sentence on the basis of BERT, the language representation model. The accuracy of the method can be sub-optimal, however, because BERT does not have sufficient capability to detect whether there is an error at each position, apparently due to the way of pre-training it using mask language modeling. In this work, we propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT, with the former being connected to the latter with what we call soft-masking technique. Our method of using `Soft-Masked BERT' is general, and it may be employed in other language detection-correction problems. Experimental results on two datasets demonstrate that the performance of our proposed method is significantly better than the baselines including the one solely based on BERT.

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