基于神经网络特征的句子级别译文质量估计

Sentence-Level Machine Translation Quality Estimation Based on Neural Network Features

机器翻译质量估计是自然语言处理中的一个重要任务,与传统的机器翻译自动评价方法不同,译文质量估计方法评估机器译文的质量不使用人工参考译文.针对目前句子级别机器译文质量估计特征提取严重依赖语言学分析导致泛化能力不足,并且制约着后续支持向量回归算法的性能,提出了利用深度学习中上下文单词预测模型和矩阵分解模型提取句子向量特征,并将其与递归神经网络语言模型特征相结合来提高译文质量自动估计与人工评价的相关性.在WMT15和WMT16译文质量估计子任务数据集上的实验结果表明:利用上下文单词预测模型提取句子向量特征的方法性能统计一致地优于传统的QuEst方法和连续空间语言模型句子向量特征提取方法,这揭示了提出的特征提取方法不仅不需要语言学分析,而且显著地提高了译文质量估计的效果.

Machine translation quality estimation is an important task in natural language processing.Unlike the traditional automatic evaluation of machine translation,the quality estimation evaluates the quality of machine translation without human reference.Nowadays,the feature extraction approaches of sentence-level quality estimation depend heavily on linguistic analysis,which leads to the lack of generalization ability and restricts the system performance of the subsequent support vector regression algorithm.In order to solve this problem,we extract sentence embedding features using context-based word prediction model and matrix decomposition model in deep learning,and enrich the features with recurrent neural network language model feature to further improve the correlation between the automatic quality estimation approach and human judgments.The experimental results on the datasets of WMT＇15 and WMT＇16 machine translation quality estimation subtasks show that the system performance of extracting the sentence embedding features by the context-based word prediction model is better than the traditional QuEst method and the approach that extracts sentence embedding features by the continuous space language model,which reveals that the proposed feature extraction approach can significantly improve the system performance of machine translation quality estimation without linguistic analysis.