Sequence Adversarial Training and Minimum Bayes Risk Decoding for End-to-end Neural Conversation Models

We present a neural conversation system that incorporates multiple sequence-to-sequence models, sequence adversarial training, example-based response selection, and BLEU-based Minimum Bayes Risk (MBR) decoding. The system was trained and tested using the 6th Dialog System Technology Challenges (DSTC6) Twitter help-desk dialog task. Experimental results demonstrate that adversarial training and the example-based method are effective in improving human rating score while system combination with MBR decoding improves objective measures such as BLEU and METEOR scores. Moreover, we investigate extension of the reward function for sequence adversarial training in order to balance subjective and objective scores.