An Alignment-Agnostic Model for C hinese Text Error Correction (2021)

Abstract: This paper investigates how to correct Chinese text errors with types of mistaken, missing and redundant characters, which are common for Chinese native speakers. Most existing models based on detect-correct framework can correct mistaken characters, but cannot handle missing or redundant characters due to inconsistency between model inputs and outputs. Although Seq2Seq-based or sequence tagging methods provide solutions to the three error types and achieved relatively good results in English context, they do not perform well in Chinese context according to our experiments. In our work, we propose a novel alignment-agnostic detect-correct framework that can handle both text aligned and non-aligned situations and can serve as a cold start model when no annotation data are provided. Experimental results on three datasets demonstrate that our method is effective and achieves a better performance than most recent published models.

## introduction chinese text error correction plays an important role in many nlp related scenarios @xcite @xcite . for native chinese speakers, common errors include mistaken characters, missing characters, and redundant characters. mistaken characters refer to wrong characters needed to be replaced. missing characters mean a lack of characters needed to be inserted into the identified position. redundant characters mean useless or repeated characters needed to be deleted. corrections for mistaken characters will not change the sentence length while corrections for the other two types will do. if texts only contain mistaken errors, we call it a text-aligned situation; if there exist missing or redundant errors, we call it a text non-aligned situation. for text-aligned situation, many approaches apply the detect-correct framework, which is to detect the positions of wrong characters first and then correct them @xcite @xcite . despite of competitive performance of such methods, they cannot deal with text non-aligned situation with missing and redundant errors. for text non-aligned situations, the reversed order error or complex structural change with multiple errors are not in our scope, first because we target to cover common mistakes made by chinese native speakers, which are different from foreign chinese learners in chinese error correction(gec) @xcite task, second because the mentioned complex errors are beyond our model settings. the two mainstream model schemes for text non-aligned situation are seq2seq-based and sequence taggingbased. the former is inspired by machine translation, which sets wrong sentences as input and correct sentences as output @xcite @xcite @xcite @xcite . such approaches require a large number of training data and may generate uncontrollable results @xcite . the latter takes wrong sentences as input and modification operations of each token as output @xcite @xcite . however, as chinese language has more than 20,000 characters that can generate many combinations of token operations, it is difficult for sequence tagging models to cover all combinations and generate results with high coverage rates. to address the above issues, we propose an alignment-agnostic detect-correct model, which can not only handle text non-aligned errors compared to the current detect-correct methods, but also can relieve the problem of huge value search space leading to uncontrollable or low coveraged results of seq2seq or sequence tagging based methods. we conduct experiments to compare our alignment-agnostic model with other models on three datasets: cged 2020, sighan 2015, sighan-synthesize. experimental results show that our model performs better than other models. the contributions of our work include (1) proposal of a novel detect-correct architecture for chinese text error correction, (2) empirical verification of the effectiveness of the alignment-agnostic model, (3) easy reproduction and fast adaptation to practical scenario with limited annotation data. ## conclusion we proposed a new detect-correct model for chinese text error correction. it can handle both textaligned and non-aligned situations, and can serve as a good baseline even in cold start situations. experimental results on three datasets show that our model performs better than existing methods. furthermore, it can be easily reproduced and achieve good results even with a small training data size, which is key to rapid application in the industry.