Paper: Fitting Sentence Level Translation Evaluation with Many Dense Features

ACL ID D14-1025
Title Fitting Sentence Level Translation Evaluation with Many Dense Features
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Sentence level evaluation in MT has turned out far more difficult than corpus level evaluation. Existing sentence level metrics employ a lim- ited set of features, most of which are rather sparse at the sentence level, and their intricate models are rarely trained for ranking. This pa- per presents a simple linear model exploiting 33 relatively dense features, some of which are novel while others are known but seldom used, and train it under the learning-to-rank frame- work. We evaluate our metric on the stan- dard WMT12 data showing that it outperforms the strong baseline METEOR. We also ana- lyze the contribution of individual features and the choice of training data, language-pair vs. target-language data, providing new insights into this task.