MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification
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by
Jianhai Zhang, Mieradilijiang Maimaiti, Xing Gao, Yuanhang Zheng, Ji Zhang
2022
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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