Paper: BLANC: Learning Evaluation Metrics For MT

ACL ID H05-1093
Title BLANC: Learning Evaluation Metrics For MT
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

We introduce BLANC, a family of dy- namic, trainable evaluation metrics for ma- chine translation. Flexible, parametrized models can be learned from past data and automatically optimized to correlate well with human judgments for different cri- teria (e.g. adequacy, fluency) using dif- ferent correlation measures. Towards this end, we discuss ACS (all common skip- ngrams), a practical algorithm with train- able parameters that estimates reference- candidate translation overlap by comput- ing a weighted sum of all common skip- ngrams in polynomial time. We show that the BLEU and ROUGE metric families are special cases of BLANC, and we compare correlations with human judgments across these three metric families. We analyze the algorithmic complexity of ACS and argue that it is more powerful ...