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

• URL: http://arxiv.org/abs/2204.04952v1
• Date: Mon, 11 Apr 2022 08:58:55 GMT
• Title: MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification
• Authors: Jianhai Zhang, Mieradilijiang Maimaiti, Xing Gao, Yuanhang Zheng, and Ji Zhang
• Abstract summary: Text classification struggles to generalize to unseen classes with very few labeled text instances per class. We propose a meta-learning based method MGIMN which performs instance-wise comparison followed by aggregation to generate class-wise matching vectors.
• Score: 9.9875634964736
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 state-of-the-art approaches, under both the standard FSL and generalized FSL settings.

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