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