SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
Table 3: The performance of our method and baseline models (%). D, C denote the detection, correction, respectively. P, R, F denote the precision, recall and F1 score, respectively. The results of BERT are from our own implementation. Best results are in bold. We performed additional fine-tuning on SIGHAN13 for 6 epochs as the data distribution in SIGHAN13 differs from other datasets, e.g. “的”, “得” and “地” are rarely distinguished.
['[EMPTY]', 'Character-level Detection-level', 'Character-level Detection-level', 'Character-level Detection-level', 'Character-level Correction-level', 'Character-level Correction-level', 'Character-level Correction-level', 'Sentence-level Detection-level', 'Sentence-level Detection-level', 'Sentence-level Detection-level', 'Sentence-level Correction-level', 'Sentence-level Correction-level', 'Sentence-level Correction-level']
When using SpellGCN, the model achieves better results in all test sets against vanilla BERT, which verifies its effectiveness. The improvement is considerable with such a large amount of training data (cf. This indicates the similarity knowledge is essential for CSC and it can hardly be learned by simply increasing the data amount. In terms of sentence-level F1score metric in the correction sub-task, i.e., C-F score in the last column, the improvements against previous best results (FASPell) are 9.2%, 9.7% and 13.3% respectively. Nevertheless, it should be noted that FASpell was trained on different training data while this paper follows the setting mentioned in the PN paper pn19. Ideally, our method is compatible with FASpell and better results can be achieved when FASpell is employed.