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First published: 2016/10/07 (8 years ago)
Abstract: Neural sequence models are widely used to model time-series data in many fields. Equally ubiquitous is the usage of beam search (BS) as an approximate inference algorithm to decode output sequences from these models. BS explores the search space in a greedy left-right fashion retaining only the top-$B$ candidates -- resulting in sequences that differ only slightly from each other. Producing lists of nearly identical sequences is not only computationally wasteful but also typically fails to capture the inherent ambiguity of complex AI tasks. To overcome this problem, we propose \emph{Diverse Beam Search} (DBS), an alternative to BS that decodes a list of diverse outputs by optimizing for a diversity-augmented objective. We observe that our method finds better top-1 solutions by controlling for the exploration and exploitation of the search space -- implying that DBS is a \emph{better search algorithm}. Moreover, these gains are achieved with minimal computational or memory overhead as compared to beam search. To demonstrate the broad applicability of our method, we present results on image captioning, machine translation and visual question generation using both standard quantitative metrics and qualitative human studies. Our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.