The performance of natural language generation systems has improved
substantially with modern neural networks. At test time they typically employ
beam search to avoid locally optimal but globally suboptimal predictions.
However, due to model errors, a larger beam size can lead to deteriorating
performance according to the evaluation metric. For this reason, it is common
to rerank the output of beam search, but this relies on beam search to produce
a good set of hypotheses, which limits the potential gains. Other alternatives
to beam search require changes to the training of the model, which restricts
their applicability compared to beam search. This paper proposes incremental
beam manipulation, i.e. reranking the hypotheses in the beam during decoding
instead of only at the end. This way, hypotheses that are unlikely to lead to a
good final output are discarded, and in their place hypotheses that would have
been ignored will be considered instead. Applying incremental beam manipulation
leads to an improvement of 1.93 and 5.82 BLEU points over vanilla beam search
for the test sets of the E2E and WebNLG challenges respectively. The proposed
method also outperformed a strong reranker by 1.04 BLEU points on the E2E
challenge, while being on par with it on the WebNLG dataset.

本论文提出了增量束搜索方法，即在解码过程中重新排列束中的假设，以提高自然语言生成系统的性能，实验结果表明，该方法相对于传统束搜索方式，在 E2E 和 WebNLG 挑战测试集中分别使 BLEU 得分提高了 1.93 和 5.82 个百分点，在 E2E 挑战测试集上也比一种强大的再排序器表现更好。