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        <title>BiSPN: Generating Entity Set and Relation Set Coherently in One Pass</title>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
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    <abstract>By modeling the interaction among instances and avoiding error propagation, Set Prediction Networks (SPNs) achieve state-of-the-art performance on the tasks of named entity recognition and relation triple extraction respectively. However, how to jointly extract entities and relation triples via SPNs remains an unexplored problem, where the main challenge is the maintenance of coherence between the predicted entity/relation sets during one-pass generation. In this work, we present Bipartite Set Prediction Network (BiSPN), a novel joint entity-relation extraction model that can efficiently generate entity set and relation set in parallel. To overcome the challenge of coherence, BiSPN is equipped with a novel bipartite consistency loss as well as an entity-relation linking loss during training. Experiments on three biomedical/clinical datasets and a general-domain dataset show that BiSPN achieves new state of the art in knowledge-intensive scene and performs competitively in general-domain, while being more efficient than two-stage joint extraction methods.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.136</identifier>
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%0 Conference Proceedings
%T BiSPN: Generating Entity Set and Relation Set Coherently in One Pass
%A He, Yuxin
%A Tang, Buzhou
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F he-tang-2023-bispn
%X By modeling the interaction among instances and avoiding error propagation, Set Prediction Networks (SPNs) achieve state-of-the-art performance on the tasks of named entity recognition and relation triple extraction respectively. However, how to jointly extract entities and relation triples via SPNs remains an unexplored problem, where the main challenge is the maintenance of coherence between the predicted entity/relation sets during one-pass generation. In this work, we present Bipartite Set Prediction Network (BiSPN), a novel joint entity-relation extraction model that can efficiently generate entity set and relation set in parallel. To overcome the challenge of coherence, BiSPN is equipped with a novel bipartite consistency loss as well as an entity-relation linking loss during training. Experiments on three biomedical/clinical datasets and a general-domain dataset show that BiSPN achieves new state of the art in knowledge-intensive scene and performs competitively in general-domain, while being more efficient than two-stage joint extraction methods.
%R 10.18653/v1/2023.findings-emnlp.136
%U https://aclanthology.org/2023.findings-emnlp.136
%U https://doi.org/10.18653/v1/2023.findings-emnlp.136
%P 2066-2077

Markdown (Informal)

[BiSPN: Generating Entity Set and Relation Set Coherently in One Pass](https://aclanthology.org/2023.findings-emnlp.136) (He & Tang, Findings 2023)

• BiSPN: Generating Entity Set and Relation Set Coherently in One Pass (He & Tang, Findings 2023)

ACL

• Yuxin He and Buzhou Tang. 2023. BiSPN: Generating Entity Set and Relation Set Coherently in One Pass. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2066–2077, Singapore. Association for Computational Linguistics.