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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.

Original languageEnglish
Title of host publicationIAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations
PublisherAssociation for the Advancement of Artificial Intelligence
Pages13081-13082
Number of pages2
ISBN (Electronic)1577358767, 9781577358763
StatePublished - 30 Jun 2022
Event36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online
Duration: 22 Feb 20221 Mar 2022

Publication series

NameProceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
Volume36

Conference

Conference36th AAAI Conference on Artificial Intelligence, AAAI 2022
CityVirtual, Online
Period22/02/221/03/22

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