  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 models for response
matching and ranking. We include the preliminary experiments and results in
this paper. This work could be further advanced with better deep matching
models for retrieval base systems or generative models for generation based
systems as well as extensive evaluations in real-life applications.
