02008nas a2200241   4500000000100000000000100001008004100002260001200043653003300055653001900088653002400107653002500131653003400156100001800190700002100208700002500229245008400254856008000338300001000418490000600428520131800434022001401752       2021                            d  c03/202110aAsynchronous Video Interview10aLanguage Model10aQuestion Generation10aConversational Agent10aFollow-up Question Generation1 aPooja Rao S B1 aManish Agnihotri1 aDinesh Babu Jayagopi00aImproving Asynchronous Interview Interaction with Follow-up Question Generation  uhttps://www.ijimai.org/journal/sites/default/files/2021-02/ijimai_6_5_8.pdf  a79-890 v63 aThe user experience of an asynchronous video interview system, conventionally is not reciprocal or conversational. Interview applicants expect that, like a typical face-to-face interview, they are innate and coherent. We posit that the planned adoption of limited probing through follow-up questions is an important step towards improving the interaction. We propose a follow-up question generation model (followQG) capable of generating relevant and diverse follow-up questions based on the previously asked questions, and their answers. We implement a 3D virtual interviewing system, Maya, with capability of follow-up question generation. Existing asynchronous interviewing systems are not dynamic with scripted and repetitive questions. In comparison, Maya responds with relevant follow-up questions, a largely unexplored feature of irtual interview systems. We take advantage of the implicit knowledge from deep pre-trained language models to generate rich and varied natural language follow-up questions. Empirical results suggest that followQG generates questions that humans rate as high quality, achieving 77% relevance. A comparison with strong baselines of neural network and rule-based systems show that it produces better quality questions. The corpus used for fine-tuning is made publicly available.  a1989-1660