PHMOS pell: Phonological and Morphological Knowledge Guided C hinese Spelling Check (2021)

Abstract: Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters. Statistics reveal that most Chinese spelling errors belong to phonological or visual errors. However, previous methods rarely utilize phonological and morphological knowledge of Chinese characters or heavily rely on external resources to model their similarities. To address the above issues, we propose a novel end-to-end trainable model called PHMOSpell, which promotes the performance of CSC with multi-modal information. Specifically, we derive pinyin and glyph representations for Chinese characters from audio and visual modalities respectively, which are integrated into a pre-trained language model by a well-designed adaptive gating mechanism. To verify its effectiveness, we conduct comprehensive experiments and ablation tests. Experimental results on three shared benchmarks demonstrate that our model consistently outperforms previous state-of-the-art models.

## introduction chinese spelling check (csc) is a fundamental task in chinese natural language processing (nlp), which aims to automatically detect and correct spelling errors in chinese sentences. these errors typically consist of human writing errors and machine recognition errors by automatic speech recognition (asr) or optical character recognition (ocr) systems @xcite . csc serves as a preliminary component for other downstream tasks like information retrieval (ir) in search engine, thus significantly affects the final performance of these tasks. chinese is an ideograph language which contains numerous characters and has no between-word delimiters. these characteristics make its spelling check more difficult than other alphabetical languages such as english. specifically, for error p-s error: wrong sentence: 人们必(pinyin: bi4) 生去追求的目标。 ground truth: 人们毕(pinyin: bi4) 生去追求的目标。 v-s error: wrong sentence: 迎接每一个固(radicals: 古,口) 难。 ground truth: 迎接每一个困(radicals: 木,口) 难。 since a great number of chinese characters are similar either in phonology or morphology, they are easily misused with each other. according to @xcite , 76% of chinese spelling errors belong to phonological similarity error and 46% belong to visual similarity error. the contributions of this paper are in three folds: 1) we derive both phonological and morphological knowledge of chinese characters from multimodality and apply them to csc. 2) we design a novel adaptive gating mechanism, which effectively incorporates the multi-modal information into a pre-trained language model in an end-to-end trainable way. 3) we achieve state-of-the-art performance on three benchmark datasets using the proposed model. ## related work csc has received active research in recent years. previous studies on csc can be divided into three categories: rule based methods, statistical based ters, there are about 216 different radicals in chinese. ## conclusion in this research, we propose a novel end-to-end trainable model called phmospell for csc, which incorporates both phonological and morphological knowledge from two feature extractors into a pretrained language model by an effective adaptive gating mechanism. extensive experiments and empirical comparisons show that phmospell achieves state-of-the-art results on three widely used benchmarks for csc, demonstrating the effectiveness of the proposed method. we remain extending the multi-modal knowledge to other nlp tasks (e.g., grammar error correction) as our future work. another fruitful future work is exploring the integration of external knowledge so that the model can deal with errors in poems, proverbs, etc.