CHATBOT using Deep Learning (Seq2Seq Models)

Gaurav Rajpoot, Arul Srivastava, Dilip Kumar, Monica Sehrawat

Many conversational agents (CAs) are developed to answer users’ questions in a specialized domain. In everyday use of CAs, user experience may extend beyond satisfying information needs to the enjoyment of conversations with CAs, some of which represent playful interactions. By studying a field deployment of a Human Resource Chabot, we report on users’ interest areas in conversational interactions to inform the development of CAs. Through the lens ofstatistical modeling, we also highlight rich signals in conversational interactions for inferring user satisfaction with the instrumental usage and playful interactions with the agent. These signals can be utilized to develop agents that adapt functionality and interaction styles. By contrasting these signals, we shed light on the varying functions of conversational interactions. We discuss design implications for CAs, and directions for developing adaptive agents based on users’ conversational behaviors.