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中文摘要：
采用回归 SVM 框架，这份报纸建议语言学激发的特征工程策略与与人的评价的更好的关联开发一个 MT 评估度量标准。与所有可得到的特征的贪婪联合的当前的惯例相对照，六个特征为翻译质量根据人的直觉被建议。然后，语言学特征的贡献经由爬山的策略被检验并且分析。实验显示与彻底的语言学特征上的评价 SVM 模型或以前的尝试相比，有六个语言学信息的回归 SVM 模型基于特征更好概括到对面不同的数据集，并且与合适的非语言学的度量标准扩充这些语言学特征能完成另外的改进。
英文摘要：
Adopting the regression SVM framework, this paper proposes a linguistically motivated feature engineering strategy to develop an MT evaluation metric with a better correlation with human assessments. In contrast to current practices of ＂greedy＂ combination of all available features, six features are suggested according to the human intuition for translation quality. Then the contribution of linguistic features is examined and analyzed via a hill-climbing strategy. Experiments indicate that, compared to either the SVM-ranking model or the previous attempts on exhaustive linguistic features, the regression SVM model with six linguistic information based features generalizes across different datasets better, and augmenting these linguistic features with proper non-linguistic metrics can achieve additional improvements.
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