  Many existing conversation models that are based on the encoder-decoder
framework have focused on ways to make the encoder more complicated to enrich
the context vectors so as to increase the diversity and informativeness of
generated responses. However, these approaches face two problems. First, the
decoder is too simple to effectively utilize the previously generated
information and tends to generate duplicated and self-contradicting responses.
Second, the complex encoder tends to generate diverse but incoherent responses
because the complex context vectors may deviate from the original semantics of
context. In this work, we proposed a conversation model named "THINK" (Teamwork
generation Hover around Impressive Noticeable Keywords) to make the decoder
more complicated and avoid generating duplicated and self-contradicting
responses. The model simplifies the context vectors and increases the coherence
of generated responses in a reasonable way. For this model, we propose Teamwork
generation framework and Semantics Extractor. Compared with other baselines,
both automatic and human evaluation showed the advantages of our model.
