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We develop the first approximate inference algorithm for 1-Best (and M-Best)decoding in bidirectional neural sequence models by extending Beam Search (BS)to reason about both forward and backward time dependencies. Beam Search (BS)is a widely used approximate inference algorithm for decoding sequences fromunidirectional neural sequence models. Interestingly, approximate inference inbidirectional models remains an open problem, despite their significantadvantage in modeling information from both the past and future. To enable theuse of bidirectional models, we present Bidirectional Beam Search (BiBS), anefficient algorithm for approximate bidirectional inference.To evaluate ourmethod and as an interesting problem in its own right, we introduce a novelFill-in-the-Blank Image Captioning task which requires reasoning about bothpast and future sentence structure to reconstruct sensible image descriptions.We use this task as well as the Visual Madlibs dataset to demonstrate theeffectiveness of our approach, consistently outperforming all baseline methods.

Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning