Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?

• URL: http://arxiv.org/abs/2310.00696v1
• Date: Sun, 1 Oct 2023 15:09:36 GMT
• Title: Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?
• Authors: Pratik Saini and Tapas Nayak and Indrajit Bhattacharya
• Abstract summary: Two distinct approaches have been proposed for relational triple extraction. Joint models, which capture interactions across triples, are the more recent development. We benchmark state-of-the-art pipeline and joint extraction models on sentence-level and document-level datasets.
• Score: 5.8309706367176295
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Two distinct approaches have been proposed for relational triple extraction - pipeline and joint. Joint models, which capture interactions across triples, are the more recent development, and have been shown to outperform pipeline models for sentence-level extraction tasks. Document-level extraction is a more challenging setting where interactions across triples can be long-range, and individual triples can also span across sentences. Joint models have not been applied for document-level tasks so far. In this paper, we benchmark state-of-the-art pipeline and joint extraction models on sentence-level as well as document-level datasets. Our experiments show that while joint models outperform pipeline models significantly for sentence-level extraction, their performance drops sharply below that of pipeline models for the document-level dataset.

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