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Most often, chat-bots are built to solve the purpose of a search engine or ahuman assistant: Their primary goal is to provide information to the user orhelp them complete a task. However, these chat-bots are incapable of respondingto unscripted queries like "Hi, what's up", "What's your favourite food". Humanevaluation judgments show that 4 humans come to a consensus on the intent of agiven query which is from chat domain only 77% of the time, thus making itevident how non-trivial this task is. In our work, we show why it is difficultto break the chitchat space into clearly defined intents. We propose a systemto handle this task in chat-bots, keeping in mind scalability,interpretability, appropriateness, trustworthiness, relevance and coverage. Ourwork introduces a pipeline for query understanding in chitchat usinghierarchical intents as well as a way to use seq-seq auto-generation models inprofessional bots. We explore an interpretable model for chat domain detectionand also show how various components such as adult/offensive classification,grammars/regex patterns, curated personality based responses, generic guidedevasive responses and response generation models can be combined in a scalableway to solve this problem.

A Trustworthy, Responsible and Interpretable System to Handle Chit Chat in Conversational Bots