Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge
This work addresses the problem of creating more personalized and knowledgeable conversational agents for users, but it is incremental as it builds on existing datasets and models.
The authors tackled the limitation of conversational agents in generating utterances that properly fuse persona and knowledge by introducing the FoCus dataset, which includes customized answers built from user persona and Wikipedia knowledge, and they evaluated models like BART and GPT-2 using automatic scores and human evaluations.
Humans usually have conversations by making use of prior knowledge about a topic and background information of the people whom they are talking to. However, existing conversational agents and datasets do not consider such comprehensive information, and thus they have a limitation in generating the utterances where the knowledge and persona are fused properly. To address this issue, we introduce a call For Customized conversation (FoCus) dataset where the customized answers are built with the user's persona and Wikipedia knowledge. To evaluate the abilities to make informative and customized utterances of pre-trained language models, we utilize BART and GPT-2 as well as transformer-based models. We assess their generation abilities with automatic scores and conduct human evaluations for qualitative results. We examine whether the model reflects adequate persona and knowledge with our proposed two sub-tasks, persona grounding (PG) and knowledge grounding (KG). Moreover, we show that the utterances of our data are constructed with the proper knowledge and persona through grounding quality assessment.