Mixed Embedding of XLM for Unsupervised Cantonese-Chinese Neural Machine Translation (Student Abstract)

Authors

  • Ka Ming Wong Dept. of Computer Science and Information Engineering, National Central University, Taiwan
  • Richard Tzong-Han Tsai Dept. of Computer Science and Information Engineering, National Central University, Taiwan

DOI:

https://doi.org/10.1609/aaai.v36i11.21677

Keywords:

Dialect Translation, Unsupervised Neural Machines Translation, Pre-trained Language Model

Abstract

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.
AAAI-22 Proceedings Cover

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Published

2022-06-28

How to Cite

Wong, K. M., & Tsai, R. T.-H. (2022). Mixed Embedding of XLM for Unsupervised Cantonese-Chinese Neural Machine Translation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13081-13082. https://doi.org/10.1609/aaai.v36i11.21677

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program