13 research outputs found
A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts
End-to-end Task-oriented Dialogue Systems (TDSs) have attracted a lot of
attention for their superiority (e.g., in terms of global optimization) over
pipeline modularized TDSs. Previous studies on end-to-end TDSs use a
single-module model to generate responses for complex dialogue contexts.
However, no model consistently outperforms the others in all cases. We propose
a neural Modular Task-oriented Dialogue System(MTDS) framework, in which a few
expert bots are combined to generate the response for a given dialogue context.
MTDS consists of a chair bot and several expert bots. Each expert bot is
specialized for a particular situation, e.g., one domain, one type of action of
a system, etc. The chair bot coordinates multiple expert bots and adaptively
selects an expert bot to generate the appropriate response. We further propose
a Token-level Mixture-of-Expert (TokenMoE) model to implement MTDS, where the
expert bots predict multiple tokens at each timestamp and the chair bot
determines the final generated token by fully taking into consideration the
outputs of all expert bots. Both the chair bot and the expert bots are jointly
trained in an end-to-end fashion. To verify the effectiveness of TokenMoE, we
carry out extensive experiments on a benchmark dataset. Compared with the
baseline using a single-module model, our TokenMoE improves the performance by
8.1% of inform rate and 0.8% of success rate.Comment: Proceedings of the 2019 SIGIR Workshop WCIS: Workshop on
Conversational Interaction System