Mixed Embedding of XLM for Unsupervised Cantonese-Chinese Neural Machine Translation (Student Abstract)
| Autor: | Ka Ming Wong, Richard Tzong-Han Tsai |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 36:13081-13082 |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v36i11.21677 |
| Popis: | Unsupervised Neural Machines Translation is the most ideal method to apply to Cantonese and Chinese translation because parallel data is scarce in this language pair. In this paper, we proposed a method that combined a modified cross-lingual language model and performed layer to layer attention on unsupervised neural machine translation. In our experiments, we observed that our proposed method does improve the Cantonese to Chinese and Chinese to Cantonese translation by 1.088 and 0.394 BLEU scores. We finally developed a web service based on our ideal approach to provide Cantonese to Chinese Translation and vice versa. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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