Abstract
Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components. These limitations inhibit generalizing capability towards Generalized Few-shot Learning settings where both seen and novel classes are co-existent. In this paper, we propose a novel Semantic Matching and Aggregation Network where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints. These semantic components capture high-level information, resulting in more effective matching between instances. Our multi-perspective matching method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances. We also propose a more challenging evaluation setting that considers classification on the joint all-class label space. Extensive experimental results demonstrate the effectiveness of our method. Our code and data are publicly available.- Anthology ID:
- 2020.findings-emnlp.108
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1209–1218
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.108
- DOI:
- 10.18653/v1/2020.findings-emnlp.108
- Cite (ACL):
- Hoang Nguyen, Chenwei Zhang, Congying Xia, and Philip Yu. 2020. Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1209–1218, Online. Association for Computational Linguistics.
- Cite (Informal):
- Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection (Nguyen et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.108.pdf
- Code
- nhhoang96/Semantic_Matching