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Chatbots, taking advantage of the success of the messaging apps and recentadvances in Artificial Intelligence, have become very popular, from helpingbusiness to improve customer services to chatting to users for the sake ofconversation and engagement (celebrity or personal bots). However, developingand improving a chatbot requires understanding their data generated by itsusers. Dialog data has a different nature of a simple question and answeringinteraction, in which context and temporal properties (turn order) creates adifferent understanding of such data. In this paper, we propose a noveltymetric to compute dialogs' similarity based not only on the text content butalso on the information related to the dialog structure. Our experimentalresults performed over the Switchboard dataset show that using evidence fromboth textual content and the dialog structure leads to more accurate resultsthan using each measure in isolation.

Combining Textual Content and Structure to Improve Dialog Similarity