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Sequence-to-sequence neural networks have been widely used in language-basedapplications as they have flexible capabilities to learn various languagemodels. However, when seeking for the optimal language response through trainedneural networks, current existing approaches such as beam-search decoderstrategies are still not able reaching to promising performances. Instead ofdeveloping various decoder strategies based on a "regular sentence order"neural network (a trained model by outputting sentences from left-to-rightorder), we leveraged "reverse" order as additional language model (a trainedmodel by outputting sentences from right-to-left order) which can providedifferent perspectives for the path finding problems. In this paper, we proposebidirectional strategies in searching paths by combining two networks(left-to-right and right-to-left language models) making a bidirectional beamsearch possible. Besides, our solution allows us using any similarity measurein our sentence selection criterion. Our approaches demonstrate betterperformance compared to the unidirectional beam search strategy.

Beam Search with Bidirectional Strategies for Neural Response Generation