Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams. This paper examines the usefulness of universal sentence representations for evaluating the quality of machine translation. Although it is difficult to train sentence representations using small-scale translation datasets with manual evaluation, sentence representations trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. Experimental results of the WMT-2016 dataset show that the proposed method achieves state-of-the-art performance with sentence representation features only.
翻译:句子表达方式可以捕捉以字符或N-grams字为基础的当地特征无法捕捉的广泛信息。本文审查了通用句子表达方式对评估机器翻译质量的用处。虽然很难使用人工评价的小规模翻译数据集对句子表达方式进行培训,但从其他任务中的大规模数据中培训的句子表达方式可以改进机器翻译的自动评价。WMT-2016数据集的实验结果显示,拟议方法仅具有句子表达方式,达到了最先进的表现方式。