Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation
Yilin Yang, Liang Huang, Mingbo Ma · 2018
Beam search is widely used in neural machine translation, and usually improves translation quality compared to greedy search.It has been widely observed that, however, beam sizes larger than 5 hurt translation quality.We explain why this happens, and propose several methods to address this problem.Furthermore, we discuss the optimal stopping criteria for these methods.Results show that our hyperparameter-free methods outperform the widely-used hyperparameter-free heuristic of length normalization by +2.0 BLEU, and achieve the best results among all methods on Chinese-to-English translation.