Abstract
Chinese grammatical error correction (CGEC), a task of correcting grammatical errors in text, is treated as a translation task, where error sentences are “translated” to correct sentences. However, some grammatical errors in the training data can confuse the CGEC models and have negative influence in the “translating” process. In this paper, we propose a Grammatical Error Weakening Module (GEWM) to impair the negative influence of grammatical errors in CGEC task. The grammatical error weakening module first extracts contextual features for each word in an error sentence via context attention mechanism. Then the proposed module uses learnable error weakening factors to control the proportion of contextual features and word features in the final representation of each word. As such, features from grammatical error words can be suppressed. Experiments show that our approach has better performance compared with the baseline models in CGEC task.
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This work was supported by National Natural Science Foundation of China (61702047) .
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Liang, J., Li, S. (2020). Weaken Grammatical Error Influence in Chinese Grammatical Error Correction. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_20
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