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

THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses