Improv Chat: Second Response Generation for Chatbot
Furu Wei

TL;DR
This paper introduces 'Improv Chat', a new task for generating second responses in chatbots to improve conversational flow, using retrieval-based systems and neural models, with preliminary experimental validation.
Contribution
It proposes a novel second response generation task, develops a retrieval-based framework, and constructs a new conversation corpus from social media for this purpose.
Findings
Developed a retrieval-based system for second response generation
Constructed a conversation corpus from public forums and social networks
Preliminary experiments show promising results for the approach
Abstract
Existing research on response generation for chatbot focuses on \textbf{First Response Generation} which aims to teach the chatbot to say the first response (e.g. a sentence) appropriate to the conversation context (e.g. the user's query). In this paper, we introduce a new task \textbf{Second Response Generation}, termed as Improv chat, which aims to teach the chatbot to say the second response after saying the first response with respect the conversation context, so as to lighten the burden on the user to keep the conversation going. Specifically, we propose a general learning based framework and develop a retrieval based system which can generate the second responses with the users' query and the chatbot's first response as input. We present the approach to building the conversation corpus for Improv chat from public forums and social networks, as well as the neural networks based…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · AI in Service Interactions · Speech and dialogue systems
