Research on Tibetan-Chinese Machine Translation Based on Multi-Strategy Processing

Saihu Liu, Jie Zhu, Zhensong Li, Zhixiang Luo
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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.
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基于多策略处理的藏汉机器翻译研究
本文以藏汉机器翻译的低资源特性为研究对象,通过多种策略获取训练数据,探索藏汉材料的领域自适应问题和多粒度分割问题。研究了基于Transformer注意机制的藏汉机器翻译方法,研究了不同分割粒度的藏汉机器翻译方法在编解码器两端的应用,评估了不同领域、不同类型的多粒度分割、语料库融合。体融合效果是BLEU评分最高的实验结果,达到44.9分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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