Keyword-driven Conversational Question Generation in Human-like Dialog Agent
Zheng Fang, Bo Wang · 2023
In daily human discourse, it is often observed that individuals disregard the correct grammatical structure while articulating their ideas. Instead, they tend to focus on brevity and providing only the essential information. Similarly, when posing questions, individuals often refrain from sharing all the relevant details of the honest answer. To address this issue, this paper introduces the "KdCQG" model. This model takes the "keywords" present in the answer as input rather than the entire answer sentence to generate conversational questions. KdCQG follows a sequence-to-sequence framework and relies on a language constraint mechanism and a coreference alignment mechanism to formulate questions based on the "keywords" present in the answer. Our analysis on the CoQA dataset shows that the KdCQG model effectively generates high-quality conversational questions even when only keywords are provided as input.