  The sequence-to-sequence (Seq2Seq) model generates target words iteratively
given the previously observed words during decoding process, which results in
the loss of the holistic semantics in the target response and the complete
semantic relationship between responses and dialogue histories. In this paper,
we propose a generic diversity-promoting joint network, called Holistic
Semantic Constraint Joint Network (HSCJN), enhancing the global sentence
information, and then regularizing the objective function with penalizing the
low entropy output. Our network introduces more target information to improve
diversity, and captures direct semantic information to better constrain the
relevance simultaneously. Moreover, the proposed method can be easily applied
to any Seq2Seq structure. Extensive experiments on several dialogue corpuses
show that our method effectively improves both semantic consistency and
diversity of generated responses, and achieves better performance than other
competitive methods.
