基于句子级BLEU指标挑选数据的半监督神经机器翻译

发布时间：2018-05-23 21:09

本文选题：半监督 + 句子级双语评估替补(BLEU)　；参考：《模式识别与人工智能》2017年10期

【摘要】：在单语语料的使用上,统计机器翻译可通过利用语言模型提高性能,而神经机器翻译很难通过这种方法有效利用单语语料.针对此问题,文中提出基于句子级双语评估替补(BLEU)指标挑选数据的半监督神经网络翻译模型.分别利用统计机器翻译和神经机器翻译模型对无标注数据生成候选翻译,然后通过句子级BLEU指标挑选单语候选翻译,加入到有标注的数据集中进行半监督联合训练.实验表明,文中方法能高效利用无标注的单语语料,在NIST汉英翻译任务上,相比仅使用精标的有标注数据单系统,文中方法 BLEU值有所提升.
[Abstract]:In the use of monolingual data, statistical machine translation can improve performance by using language models, while neural machine translation is difficult to effectively use monolingual data. To solve this problem, a semi-supervised neural network translation model based on sentence level bilingual evaluation index selection data is proposed in this paper. Statistical machine translation model and neural machine translation model are used to generate candidate translation for untagged data respectively. Then single language candidate translation is selected by sentence level BLEU index and added to annotated data set for semi-supervised joint training. Experiments show that the proposed method can efficiently use unannotated monolingual data, and the BLEU value of the proposed method is improved compared with that of the NIST Chinese-English translation task.
【作者单位】：中国科学技术大学语音及语言信息处理国家工程实验室;
【基金】：国家重点研发计划专项项目(No.2016YFB1001303)资助~~
【分类号】：H085

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