Qingyue Wang


2023

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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
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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 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 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 parameters.

2022

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Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking
Qingyue Wang | Yanan Cao | Piji Li | Yanhe Fu | Zheng Lin | Li Guo
Proceedings of the 29th International Conference on Computational Linguistics