  Being able to generate informative and coherent dialogue responses is crucial
when designing human-like open-domain dialogue systems. Encoder-decoder-based
dialogue models tend to produce generic and dull responses during the decoding
step because the most predictable response is likely to be a non-informative
response instead of the most suitable one. To alleviate this problem, we
propose to train the generation model in a bidirectional manner by adding a
backward reasoning step to the vanilla encoder-decoder training. The proposed
backward reasoning step pushes the model to produce more informative and
coherent content because the forward generation step's output is used to infer
the dialogue context in the backward direction. The advantage of our method is
that the forward generation and backward reasoning steps are trained
simultaneously through the use of a latent variable to facilitate bidirectional
optimization. Our method can improve response quality without introducing side
information (e.g., a pre-trained topic model). The proposed bidirectional
response generation method achieves state-of-the-art performance for response
quality.
