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

ACL ID P07-1111
Title A Re-examination of Machine Learning Approaches for Sentence-Level MT Evaluation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

Recent studies suggest that machine learn- ing can be applied to develop good auto- matic evaluation metrics for machine trans- lated sentences. This paper further ana- lyzes aspects of learning that impact per- formance. We argue that previously pro- posed approaches of training a Human- Likeness classifier is not as well correlated with human judgments of translation qual- ity, but that regression-based learning pro- duces more reliable metrics. We demon- strate the feasibility of regression-based metrics through empirical analysis of learn- ing curves and generalization studies and show that they can achieve higher correla- tions with human judgments than standard automatic metrics.