Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts
  for Zero-Shot Dialogue State Tracking
Qingyue Wang Liang Ding Yanan Cao Yibing Zhan Zheng Lin Shi Wang Dacheng Tao Li Guo
Zero-shot transfer learning for Dialogue State Tracking (DST) helps to handle
a variety of task-oriented dialogue domains without the cost of collecting
in-domain data. Existing works mainly study common data- or model-level
augmentation methods to enhance the generalization but fail to effectively
decouple the semantics of samples, limiting the zero-shot performance of DST.
In this paper, we present a simple and effective "divide, conquer and combine"
solution, which explicitly disentangles the semantics of seen data, and
leverages the performance and robustness with the mixture-of-experts mechanism.
Specifically, we divide the seen data into semantically independent subsets and
train corresponding experts, the newly unseen samples are mapped and inferred
with mixture-of-experts with our designed ensemble inference. Extensive
experiments on MultiWOZ2.1 upon the T5-Adapter show our schema significantly
and consistently improves the zero-shot performance, achieving the SOTA on
settings without external knowledge, with only 10M trainable parameters1.