  Dialogue response generation (DRG) is a critical component of task-oriented
dialogue systems (TDSs). Its purpose is to generate proper natural language
responses given some context, e.g., historical utterances, system states, etc.
State-of-the-art work focuses on how to better tackle DRG in an end-to-end way.
Typically, such studies assume that each token is drawn from a single
distribution over the output vocabulary, which may not always be optimal.
Responses vary greatly with different intents, e.g., domains, system actions.
  We propose a novel mixture-of-generators network (MoGNet) for DRG, where we
assume that each token of a response is drawn from a mixture of distributions.
MoGNet consists of a chair generator and several expert generators. Each expert
is specialized for DRG w.r.t. a particular intent. The chair coordinates
multiple experts and combines the output they have generated to produce more
appropriate responses. We propose two strategies to help the chair make better
decisions, namely, a retrospective mixture-of-generators (RMoG) and prospective
mixture-of-generators (PMoG). The former only considers the historical
expert-generated responses until the current time step while the latter also
considers possible expert-generated responses in the future by encouraging
exploration. In order to differentiate experts, we also devise a
global-and-local (GL) learning scheme that forces each expert to be specialized
towards a particular intent using a local loss and trains the chair and all
experts to coordinate using a global loss.
  We carry out extensive experiments on the MultiWOZ benchmark dataset. MoGNet
significantly outperforms state-of-the-art methods in terms of both automatic
and human evaluations, demonstrating its effectiveness for DRG.

Computation and Language;Artificial Intelligence;Information Retrieval