Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection

Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip Yu


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
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1209–1218
Language:
URL:
https://www.aclweb.org/anthology/2020.findings-emnlp.108
DOI:
10.18653/v1/2020.findings-emnlp.108
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.findings-emnlp.108.pdf
Optional supplementary material:
 2020.findings-emnlp.108.OptionalSupplementaryMaterial.zip