句对齐研究综述

神经机器翻译是目前机器翻译领域的主流方法,拥有足够数量的双语平行语料是训练出一个好的翻译模型的前提。双语句对齐技术作为一种从不同语言端单语语料中获取双语平行句对的技术,因此得到广泛的研究。该文首先简单介绍句对齐任务及其相应的评测标准,然后归纳总结前人在句对齐任务上的研究进展,以及句对齐任务的相关信息,并简单概括参加团队所提交的系统,最后对当前工作进行总结并展望未来的工作。

A Survey of Sentence Alignment

Neural machine translation has achieved good translation results on languages with abundant corpus, but it has poor performance on languages with scarce bilingual corpus resources such as Chinese-Vietnamese, this problem can be better alleviated by generating pseudo-parallel sentence pairs through word-level replacement of existing small-scale bilingual data. Considering the problem of multiple translations of one word in Chinese-Vietnamese word-level substitutions, so we studied the replacement based on larger granularity, and proposed the Chinese-Vietnamese pseudo-parallel sentence pair generation method based on phrase substitution. Use small-scale bilingual data for phrase extraction to construct a phrase alignment table, and expand it with entity phrases extracted from Wikipedia, after performing phrase recognition on bilingual data for Chinese and Vietnamese, use the phrase pair in the phrase alignment table that is more similar to the recognized phrase to replace, to achieve the phrase-level data enhancement, and train the final neural machine translation model together with the generated pseudo-parallel sentence pairs and the original data. Experimental results on Chinese-Vietnamese translation tasks show that pseudo-parallel sentence pairs generated by phrase substitution can effectively improve the performance of Chinese-Vietnamese neural machine translation.