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In neural text generation such as neural machine translation, summarization,and image captioning, beam search is widely used to improve the output textquality. However, in the neural generation setting, hypotheses can finish indifferent steps, which makes it difficult to decide when to end beam search toensure optimality. We propose a provably optimal beam search algorithm thatwill always return the optimal-score complete hypothesis (modulo beam size),and finish as soon as the optimality is established (finishing no later thanthe baseline). To counter neural generation's tendency for shorter hypotheses,we also introduce a bounded length reward mechanism which allows a modifiedversion of our beam search algorithm to remain optimal. Experiments on neuralmachine translation demonstrate that our principled beam search algorithm leadsto improvement in BLEU score over previously proposed alternatives.

When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size)