Abstract
Grammar Error Correction (GEC) is an important researching subject among Nature Language Processing tasks. In this work, aiming at tackling with genitive and ergative grammatical errors in Tibetan formal text, we collect 1793563 consecutive sentence pairs as training set and 5000 sentence pairs with the same distribution as well as 1159 sentence pairs in different distributions as testing sets. In our approach, we firstly preprocess Tibetan text data with compositional rules and then build a neural network architecture which is a combination of BERT and Bi-LSTM, to estimate the probability of given token being genitive or ergative. In experiments, 98.38% and 86.16% in terms of accuracy are observed respectively in testing the proposed model on two different testing sets.
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Acknowledgement
National Key R&D Program of China (2017YFB1402200), The National Natural Science Foundation of China (61063033, 61662061), The National Social Science Fund of China (14BYY132).
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Jiacuo, C., Jia, S., Duanzhu, S., Jia, C. (2020). Tibetan Case Grammar Error Correction Method Based on Neural Networks. In: Hong, JF., Zhang, Y., Liu, P. (eds) Chinese Lexical Semantics. CLSW 2019. Lecture Notes in Computer Science(), vol 11831. Springer, Cham. https://doi.org/10.1007/978-3-030-38189-9_43
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DOI: https://doi.org/10.1007/978-3-030-38189-9_43
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