Word Confidence Estimation for SMT N-best List Re-ranking
Ngoc Quang Luong, Laurent Besacier, Benjamin Lecouteux · 2014
This paper proposes to use Word Confidence Estimation (WCE) information to improve MT outputs via N-best list reranking.From the confidence label assigned for each word in the MT hypothesis, we add six scores to the baseline loglinear model in order to re-rank the N-best list.Firstly, the correlation between the WCE-based sentence-level scores and the conventional evaluation scores (BLEU, TER, TERp-A) is investigated.Then, the N-best list re-ranking is evaluated over different WCE system performance levels: from our real and efficient WCE system (ranked 1st during last WMT 2013 Quality Estimation Task) to an oracle WCE (which simulates an interactive scenario where a user simply validates words of a MT hypothesis and the new output will be automatically re-generated).The results suggest that our real WCE system slightly (but significantly) improves the baseline while the oracle one extremely boosts it; and better WCE leads to better MT quality.