Name
Abstract | ||
|---|---|---|
Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of attention. In this paper, we focus on the FSL problem of dialogue understanding, which contains two closely related tasks: intent detection and slot filling. Dialogue understanding has been proven to benefit a lot from jointly learning the two sub-tasks. However, such joint learning becomes challenging in the few-shot scenarios: on the one hand, the sparsity of samples greatly magnifies the difficulty of modeling the connection between the two tasks; on the other hand, how to jointly learn multiple tasks in the few-shot setting is still less investigated. In response to this, we introduce FewJoint, the first FSL benchmark for joint dialogue understanding. FewJoint provides a new corpus with 59 different dialogue domains from real industrial API and a code platform to ease FSL experiment set-up, which are expected to advance the research of this field. Further, we find that insufficient performance of the few-shot setting often leads to noisy sharing between two sub-task and disturbs joint learning. To tackle this, we guide slot with explicit intent information and propose a novel trust gating mechanism that blocks low-confidence intent information to ensure high quality sharing. Besides, we introduce a Reptile-based meta-learning strategy to achieve better generalization in unseen few-shot domains. In the experiments, the proposed method brings significant improvements on two datasets and achieve new state-of-the-art performance. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s13042-022-01604-9 | International Journal of Machine Learning and Cybernetics |
Keywords | DocType | Volume |
Few-shot learning, Joint learning, Dialogue understanding | Journal | 13 |
Issue | ISSN | Citations |
11 | 1868-8071 | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yutai Hou | 1 | 3 | 3.43 |
| Xinghao Wang | 2 | 0 | 0.34 |
| Cheng Chen | 3 | 0 | 0.34 |
| Bohan Li | 4 | 0 | 0.34 |
| Wanxiang Che | 5 | 711 | 66.39 |
| Zhigang Chen | 6 | 204 | 34.10 |