{"title":"Research on Tibetan-Chinese Machine Translation Based on Multi-Strategy Processing","authors":"Saihu Liu, Jie Zhu, Zhensong Li, Zhixiang Luo","doi":"10.1109/PRML52754.2021.9520733","DOIUrl":null,"url":null,"abstract":"This article takes the low-resource nature of Tibetan-Chinese machine translation as the research object, acquires training data through a variety of strategies, and explores the problem of domain adaptability in Tibetan-Chinese materials and the problem of multi-granularity segmentation. Researched the Tibetan-Chinese machine translation method based on Transformer attention mechanism, studied the Tibetan-Chinese machine translation method with different segmentation granularity applied to both ends of encoder-decoder, evaluated multiple granular segmentation, corpus fusion of different fields and different types. The effect of corpus fusion is the experimental result with the highest BLEU score of 44.9 points.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRML52754.2021.9520733","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This article takes the low-resource nature of Tibetan-Chinese machine translation as the research object, acquires training data through a variety of strategies, and explores the problem of domain adaptability in Tibetan-Chinese materials and the problem of multi-granularity segmentation. Researched the Tibetan-Chinese machine translation method based on Transformer attention mechanism, studied the Tibetan-Chinese machine translation method with different segmentation granularity applied to both ends of encoder-decoder, evaluated multiple granular segmentation, corpus fusion of different fields and different types. The effect of corpus fusion is the experimental result with the highest BLEU score of 44.9 points.