To gather the benefits of both approaches, in this paper, we propose an adaptive neural dialogue generation model which utilizes a single encoderdecoder for diverse conversations, meanwhile, the encoder-decoder is specifically parameterized according to each conversation.
we propose two adaptive parameterization mechanisms: 1) context-aware parameterization directly generates parameters of the encoder-decoder model by capturing local semantics of the given context; 2) topic-aware parameterization enables parameter sharing among conversations with similar topic distributions by first inferring the latent topics of the input context, and then generating the parameters with respect to the inferred latent topics.