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

Anonymous

Paper Link: https://openreview.net/forum?id=6rSRL11c365

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

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.

Presentation Mode: This paper will be presented virtually

Virtual Presentation Timezone: UTC-8

Copyright Consent Signature (type Name Or NA If Not Transferrable): Jianhai Zhang

Copyright Consent Name And Address: Alibaba Group, No. 969, Wenyi West Road, Yuhang District, Hangzhou, China