Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation
Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods.
['Small beam ( [ITALIC] b=14,15,16)', 'dev BLEU', 'dev ratio', 'test BLEU', 'test ratio']
Without loss of generality, we show results with both small and large beam sizes, which average over b=14,15,16 and b=39,40,41, respectively. On the other hand, results from length normalization method show that optimal stopping technique gains significant improvement by around +0.9 BLEU. While with both, our proposed methods beat all previous methods, and gain improvement over hyperparameter-free baseline (i.e. length normalization) by +2.0 BLEU.