  Target-guided response generation enables dialogue systems to smoothly
transition a conversation from a dialogue context toward a target sentence.
Such control is useful for designing dialogue systems that direct a
conversation toward specific goals, such as creating non-obtrusive
recommendations or introducing new topics in the conversation. In this paper,
we introduce a new technique for target-guided response generation, which first
finds a bridging path of commonsense knowledge concepts between the source and
the target, and then uses the identified bridging path to generate transition
responses. Additionally, we propose techniques to re-purpose existing dialogue
datasets for target-guided generation. Experiments reveal that the proposed
techniques outperform various baselines on this task. Finally, we observe that
the existing automated metrics for this task correlate poorly with human
judgement ratings. We propose a novel evaluation metric that we demonstrate is
more reliable for target-guided response evaluation. Our work generally enables
dialogue system designers to exercise more control over the conversations that
their systems produce.
