Deep Conversational Recommender in Travel
TABLE I: Human evaluation results for different methods.
['[BOLD] Method', '[BOLD] Fluency', '[BOLD] Informativeness', '[BOLD] Ranking']
It directly reflects human perception of the quality of generated responses. The results show that the proposed DCR achieves the best performance across these various metrics, which indicates that the responses generated by it are more fluent and informative. We show that the performance improvements of DCR over the other methods are significant. For example, in terms of the Fluency score, DCR improves the performance of response generation by 50.0%, 44.5%, 30.3%, 53.5% and 8.8% as compared to the HRED, MultiWoz, Mem2Seq, Redial and TopicRNN methods, respectively. Intuitively, at a certain degree, the BLEU score also reflects how fluent the responses are. In the results, these two metrics indeed show similar pattern of performance improvements. As detailed before, the main reason for the superior performance of DCR might be due to the global topic control mechanism. In travel, dialogs naturally involve multiple sub-tasks, which leads to several topics in the dialog flow. The topic control component enables the system to swiftly switch among topics and generate within-topic responses.