In this paper, we propose a simple, fast decoding algorithm that fosters
diversity in neural generation. The algorithm modifies the standard beam search
algorithm by adding an inter-sibling ranking penalty, favoring choosing
hypotheses from diverse parents. We evaluate the proposed model on the tasks of
dialogue response generation, abstractive summarization and machine
translation. We find that diverse decoding helps across all tasks, especially
those for which reranking is needed.
We further propose a variation that is capable of automatically adjusting its
diversity decoding rates for different inputs using reinforcement learning
(RL). We observe a further performance boost from this RL technique. This paper
includes material from the unpublished script "Mutual Information and Diverse
Decoding Improve Neural Machine Translation" (Li and Jurafsky, 2016).

该论文提出了一种简单快速的解码算法，在神经生成过程中促进多样性，评估结果表明，多样性解码有助于各种任务，特别是在需要重新排名的任务中，并进一步提出了一种能够自动调整不同输入的多样性解码速率的变化，通过强化学习技术实现，观察到进一步的性能提升。