IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification

• URL: http://arxiv.org/abs/2209.11407v1
• Date: Fri, 23 Sep 2022 04:50:47 GMT
• Title: IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification
• Authors: Ziyuan Wang, Hailiang Huang, Songqiao Han
• Abstract summary: We propose a novel model structure via siamese BERT and interactive double attentions named IDEA to capture the information exchange of text and label names. Our proposed method outperforms the state-of-the-art methods using label texts significantly with more stable results.
• Score: 4.342189319523322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in the label text. As a matter of fact, humans classify documents primarily based on the semantic meaning of the subcategories. We propose a novel model structure via siamese BERT and interactive double attentions named IDEA ( Interactive DoublE Attentions) to capture the information exchange of text and label names. Interactive double attentions enable the model to exploit the inter-class and intra-class information from coarse to fine, which involves distinguishing among all labels and matching the semantical subclasses of ground truth labels. Our proposed method outperforms the state-of-the-art methods using label texts significantly with more stable results.

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