Abstract
Many real-world open-domain conversation
applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study
the problem of imposing conversational goals
on open-domain chat agents. In particular, we
want a conversational system to chat naturally
with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that
introduces coarse-grained keywords to control the intended content of system responses.
We then attain smooth conversation transition
through turn-level supervised learning, and
drive the conversation towards the target with
discourse-level constraints. We further derive
a keyword-augmented conversation dataset for
the study. Quantitative and human evaluations
show our system can produce meaningful and
effective conversations, significantly improving over other approaches