Papers with generative or information compression models

1 papers
Span-based Named Entity Recognition by Generating and Compressing Information (2023.eacl-main)

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Challenge: Existing work on Named Entity Recognition (NER) only used generative or information compression models to improve performance.
Approach: They propose to combine two types of IB models into one system to enhance Named Entity Recognition (NER) they incorporate unsupervised generative components span reconstruction and synonym generation into a span-based NER system.
Outcome: The proposed model focuses on learning span representation, which is applicable not only to span-based NER but also to other span-related tasks such as event coreference resolution and question answering.

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