拼写错误纠正是一项重要而又具有挑战性的任务，因为令人满意的解决方案本质上需要人类水平的语言理解能力。在不失一般性的前提下，我们在本文中考虑了中文拼写错误纠正（CSC）。用于该任务的最新方法是基于语言表示模型BERT从句子的每个位置的待纠正候选对象列表中选择一个字符。但是，由于BERT并没有足够的能力来检测每个位置是否存在错误，因此该方法的准确性可能不是最佳的，这显然是由于使用掩码语言建模进行预训练的方式所致。在这项工作中，我们提出了一种新颖的神经体系结构来解决上述问题，它由一个基于BERT的错误检测网络和一个纠错网络组成，前者通过所谓的软屏蔽技术与后者连接。我们使用“ Soft-Masked BERT”的方法是通用的，并且可以在其他语言检测校正问题中使用。在两个数据集上的实验结果表明，我们提出的方法的性能明显好于基线，包括仅基于BERT的基线。

原文标题：Spelling Error Correction with Soft-Masked BERT

原文：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.

原文作者：Shaohua Zhang, Haoran Huang, Jicong Liu, Hang Li

原文地址：https://arxiv.org/abs/2005.07421