Abstract

We propose a simple, fast decoding algorithm that fosters diversity in neural generation. The algorithm modifies the standard beam search algorithm by penalizing hypotheses that are siblings---expansions of the same parent node in the search---thus favoring including hypotheses from diverse parents. We evaluate the model on three neural generation tasks: dialogue response generation, abstractive summarization, and machine translation. We also describe an extended model that uses reinforcement learning to automatically choose the appropriate level of beam diversity for different inputs or tasks. Simple diverse decoding helps across all three tasks, especially those needing reranking or having diverse ground truth outputs; reinforcement learning offers an additional boost. 11 1 This paper includes material from the unpublished manuscript “Mutual Information and Diverse Decoding Improve Neural Machine Translation” (Li and Jurafsky, 2016).