A Re-examination of Machine Learning Approaches for Sentence-Level MT Evaluation

Recent studies suggest that machine learning can be applied to develop good automatic evaluation metrics for machine translated sentences. This paper further analyzes aspects of learning that impact performance. We argue that previously proposed approaches of training a HumanLikeness classifier is not as well correlated with human judgments of translation quality, but that regression-based learning produces more reliable metrics. We demonstrate the feasibility of regression-based metrics through empirical analysis of learning curves and generalization studies and show that they can achieve higher correlations with human judgments than standard automatic metrics.

• Type: Conference
• Year: 2007
• Where: ACL
• Authors: Joshua Albrecht, Rebecca Hwa
• Keywords: ACL 2007, Automatic Evaluation Metrics, Computational Linguistics, Human Judgments, Standard Automatic Metrics