Identifying users' domain expertise from dialogues

Abstract

Nowadays, many companies are offering chatbots and voicebots to their customers. Despite much recent success in natural language processing and dialogue research, the communication between a human and a machine is still in its infancy. In this context, dialogue personalization could be a key to bridge some of the gap, making sense of users' experiences, needs, interests and mental models when engaged in a conversation. On this line, we propose to automatically learn user's features directly from the dialogue with the chatbot, in order to enable the adaptation of the response accordingly and thus improve the interaction with the user. In this paper, we focus on the user's domain expertise and, assuming that expertise affects linguistic features of the language, we propose a vocabulary-centered model joint with a Deep Learning method for the automatic classification of the users expertise at word- and message-level. An experimentation over 5000 real messages taken from a telco commercial chatbot carried to high accuracy scores, demonstrating the feasibility of the proposed task and paving the way for novel user-aware applications.