  Open-domain response generation is the task of generating sensible and
informative re-sponses to the source sentence. However, neural models tend to
generate safe and mean-ingless responses. While cue-word introducing approaches
encourage responses with concrete semantics and have shown tremendous
potential, they still fail to explore di-verse responses during decoding. In
this paper, we propose a novel but natural approach that can produce multiple
cue-words during decoding, and then uses the produced cue-words to drive
decoding and shrinks the decoding vocabulary. Thus the neural genera-tion model
can explore the full space of responses and discover informative ones with
efficiency. Experimental results show that our approach significantly
outperforms several strong baseline models with much lower decoding complexity.
Especially, our approach can converge to concrete semantics more efficiently
during decoding.
