引入源端信息的机器译文自动评价方法研究

机器译文自动评价是机器翻译中的一个重要任务。针对目前译文自动评价中完全忽略源语言句子信息，仅利用人工参考译文度量翻译质量的不足，该文提出了引入源语言句子信息的机器译文自动评价方法: 从机器译文与其源语言句子组成的二元组中提取描述翻译质量的质量向量，并将其与基于语境词向量的译文自动评价方法利用深度神经网络进行融合。在WMT-19译文自动评价任务数据集上的实验结果表明，该文所提出的方法能有效增强机器译文自动评价与人工评价的相关性。深入的实验分析进一步揭示了源语言句子信息在译文自动评价中发挥着重要作用。

Research on Incorporating the Source Information to Automatic Evaluation of Machine Translation

Automatic evaluation of machine translation is a key issue in machine translation. In existing work, the source sentence information is completely ignored and only the reference is used to measure the translation quality. This paper presents a novel automatic evaluation metric incorporating the source information: extracting the quality embeddings that describes the translation quality from a tuple consist of the machine translations and their corresponding source sentences, and incorporating it into the automatic evaluation method based on contextual embeddings by using a deep neural network. The experimental results on the dataset of WMT-19 Metrics task show that the proposed method can effectively enhance the evaluation correlation with the human judgments. Deep analysis further reveals that the information of the source sentences plays an important role in automatic evaluation of machine translation.