So-Eon Kim  Choong Seon Hong  Seong-Bae Park

10.5626/JOK.2022.49.8.601

conversational response generator query token importance weight calculator open-domain dialogue Sequence-to-Sequence Model Loss Function

The conversational response generator(CRG) has made great progress through the sequence-to-sequence model, but it often generates an over-general response which can be a response to all queries or an inappropriate response. Some efforts have been made to modify the traditional loss function to solve this problem and reduce the generation of irrelevant responses to the query by solving the problem of the lack of background knowledge of the CRG, but they did not solve both problems. This paper propose the use of a query token importance calculator because the cause of generating unrelated and overly general responses is that the CRG does not capture the core of the query. Also, based on the theory that the questioner induces a specific response from the listener and designs the speech, this paper proposes to use the golden response to understand the core meaning of the query. The qualitative evaluation confirmed that the response generator using the proposed model was able to generate responses related to the query compared to the model that did not use the proposed model.