MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification


Please cite:
title={MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification},
author={Jianhai Zhang, Mieradilijiang Maimaiti, Xing Gao, Yuanhang Zheng, and Ji Zhang},
journal={International Conference on North American Chapter of the Association for Computational Linguistics (NAACL)},


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 \textbf{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 state-of-the-art approaches, under both the standard FSL and generalized FSL settings.