  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.
