MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification
Jianhai Zhang, Mieradilijiang Maimaiti, Gao Xing, Yuanhang Zheng, Ji Zhang
Abstract
Text classification struggles to generalize to unseen classes with very few labeled text instances per class. In such a few-shot learning (FSL) setting, metric-based meta-learning approaches have shown promising results. Previous studies mainly aim to derive a prototype representation for each class. However, they neglect that it is challenging-yet-unnecessary to construct a compact representation which expresses the entire meaning for each class. They also ignore the importance to capture the inter-dependency between query and the support set for few-shot text classification. To deal with these issues, we propose a meta-learning based method MGIMN which performs instance-wise comparison followed by aggregation to generate class-wise matching vectors instead of prototype learning. The key of instance-wise comparison is the interactive matching within the class-specific context and episode-specific context. Extensive experiments demonstrate that the proposed method significantly outperforms the existing SOTA approaches, under both the standard FSL and generalized FSL settings.- Anthology ID:
- 2022.naacl-main.141
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1937–1946
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.141
- DOI:
- 10.18653/v1/2022.naacl-main.141
- Cite (ACL):
- Jianhai Zhang, Mieradilijiang Maimaiti, Gao Xing, Yuanhang Zheng, and Ji Zhang. 2022. MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1937–1946, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification (Zhang et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.naacl-main.141.pdf