  Recently, open-domain dialogue systems have attracted growing attention. Most
of them use the sequence-to-sequence (Seq2Seq) architecture to generate
responses. However, traditional Seq2Seq-based open-domain dialogue models tend
to generate generic and safe responses, which are less informative, unlike
human responses. In this paper, we propose a simple but effective
keywords-guided Sequence-to-Sequence model (KW-Seq2Seq) which uses keywords
information as guidance to generate open-domain dialogue responses.
Specifically, KW-Seq2Seq first uses a keywords decoder to predict some topic
keywords, and then generates the final response under the guidance of them.
Extensive experiments demonstrate that the KW-Seq2Seq model produces more
informative, coherent and fluent responses, yielding substantive gain in both
automatic and human evaluation metrics.
