TY - GEN
T1 - Using Grammatical and Semantic Correction Model to Improve Chinese-to-Taiwanese Machine Translation Fluency
AU - Li, Yuan Han
AU - Young, Chung Ping
AU - Lu, Wen Hsiang
N1 - Publisher Copyright:
© 2022 the Association for Computational Linguistics and Chinese Language Processing (ACLCLP).
PY - 2022
Y1 - 2022
N2 - Currently, there are three major issues to tackle in Chinese-to-Taiwanese machine translation: multi-pronunciation Taiwanese words, unknown words, and Chinese-to-Taiwanese grammatical and semantic transformation. Recent studies have mostly focused on the issues of multi-pronunciation Taiwanese words and unknown words, while very few research papers focus on grammatical and semantic transformation. However, there exist grammatical rules exclusive to Taiwanese that, if not translated properly, would cause the result to feel unnatural to native speakers and potentially twist the original meaning of the sentence, even with the right words and pronunciations. Therefore, this study collects and organizes a few common Taiwanese sentence structures and grammar rules, then creates a grammar and semantic correction model for Chinese-to-Taiwanese machine translation, which would detect and correct grammatical and semantic discrepancies between the two languages, thus improving translation fluency.
AB - Currently, there are three major issues to tackle in Chinese-to-Taiwanese machine translation: multi-pronunciation Taiwanese words, unknown words, and Chinese-to-Taiwanese grammatical and semantic transformation. Recent studies have mostly focused on the issues of multi-pronunciation Taiwanese words and unknown words, while very few research papers focus on grammatical and semantic transformation. However, there exist grammatical rules exclusive to Taiwanese that, if not translated properly, would cause the result to feel unnatural to native speakers and potentially twist the original meaning of the sentence, even with the right words and pronunciations. Therefore, this study collects and organizes a few common Taiwanese sentence structures and grammar rules, then creates a grammar and semantic correction model for Chinese-to-Taiwanese machine translation, which would detect and correct grammatical and semantic discrepancies between the two languages, thus improving translation fluency.
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M3 - Conference contribution
AN - SCOPUS:85154585267
T3 - ROCLING 2022 - Proceedings of the 34th Conference on Computational Linguistics and Speech Processing
SP - 75
EP - 83
BT - ROCLING 2022 - Proceedings of the 34th Conference on Computational Linguistics and Speech Processing
A2 - Chang, Yung-Chun
A2 - Huang, Yi-Chin
A2 - Wu, Jheng-Long
A2 - Su, Ming-Hsiang
A2 - Huang, Hen-Hsen
A2 - Liu, Yi-Fen
A2 - Lee, Lung-Hao
A2 - Chou, Chin-Hung
A2 - Liao, Yuan-Fu
PB - The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
T2 - 34th Conference on Computational Linguistics and Speech Processing, ROCLING 2022
Y2 - 21 November 2022 through 22 November 2022
ER -