Using Grammatical and Semantic Correction Model to Improve Chinese-to-Taiwanese Machine Translation Fluency

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題ROCLING 2022 - Proceedings of the 34th Conference on Computational Linguistics and Speech Processing
編輯Yung-Chun Chang, Yi-Chin Huang, Jheng-Long Wu, Ming-Hsiang Su, Hen-Hsen Huang, Yi-Fen Liu, Lung-Hao Lee, Chin-Hung Chou, Yuan-Fu Liao
發行者The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
頁面75-83
頁數9
ISBN(電子)9789869576956
出版狀態Published - 2022
事件34th Conference on Computational Linguistics and Speech Processing, ROCLING 2022 - Taipei, Taiwan
持續時間: 2022 11月 212022 11月 22

出版系列

名字ROCLING 2022 - Proceedings of the 34th Conference on Computational Linguistics and Speech Processing

Conference

Conference34th Conference on Computational Linguistics and Speech Processing, ROCLING 2022
國家/地區Taiwan
城市Taipei
期間22-11-2122-11-22

All Science Journal Classification (ASJC) codes

  • 語言與語言學
  • 言語和聽力

指紋

深入研究「Using Grammatical and Semantic Correction Model to Improve Chinese-to-Taiwanese Machine Translation Fluency」主題。共同形成了獨特的指紋。

引用此