Papers with UTCD

1 papers
Label Agnostic Pre-training for Zero-shot Text Classification (2023.findings-acl)

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Challenge: Existing approaches to text classification assume a fixed set of labels . however, in real-world applications, there exists an infinite label space for describing a given text .
Approach: They propose two new methods that inject aspect-level understanding into pre-trained models at train time to improve zero-shot generalization.
Outcome: The proposed methods improve zero-shot generalization on a set of challenging datasets.

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