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    <abstract>Attribute extraction aims to identify attribute names and the corresponding values from descriptive texts, which is the foundation for extensive downstream applications such as knowledge graph construction, search engines, and e-Commerce. In previous studies, attribute extraction is generally treated as a classification problem for predicting attribute types or a sequence tagging problem for labeling attribute values, where two paradigms, i.e., closed-world and open-world assumption, are involved. However, both of these paradigms have limitations in terms of real-world applications. And prior studies attempting to integrate these paradigms through ensemble, pipeline, and co-training models, still face challenges like cascading errors, high computational overhead, and difficulty in training. To address these existing problems, this paper presents Attribute Tree, a unified formulation for real-world attribute extraction application, where closed-world, open-world, and semi-open attribute extraction tasks are modeled uniformly. Then a text-to-tree generation model, AtTGen, is proposed to learn annotations from different scenarios efficiently and consistently. Experiments demonstrate that our proposed paradigm well covers various scenarios for real-world applications, and the model achieves state-of-the-art, outperforming existing methods by a large margin on three datasets. Our code, pretrained model, and datasets are available at https://github.com/lsvih/AtTGen.</abstract>
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%0 Conference Proceedings
%T AtTGen: Attribute Tree Generation for Real-World Attribute Joint Extraction
%A Li, Yanzeng
%A Xue, Bingcong
%A Zhang, Ruoyu
%A Zou, Lei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F li-etal-2023-attgen
%X Attribute extraction aims to identify attribute names and the corresponding values from descriptive texts, which is the foundation for extensive downstream applications such as knowledge graph construction, search engines, and e-Commerce. In previous studies, attribute extraction is generally treated as a classification problem for predicting attribute types or a sequence tagging problem for labeling attribute values, where two paradigms, i.e., closed-world and open-world assumption, are involved. However, both of these paradigms have limitations in terms of real-world applications. And prior studies attempting to integrate these paradigms through ensemble, pipeline, and co-training models, still face challenges like cascading errors, high computational overhead, and difficulty in training. To address these existing problems, this paper presents Attribute Tree, a unified formulation for real-world attribute extraction application, where closed-world, open-world, and semi-open attribute extraction tasks are modeled uniformly. Then a text-to-tree generation model, AtTGen, is proposed to learn annotations from different scenarios efficiently and consistently. Experiments demonstrate that our proposed paradigm well covers various scenarios for real-world applications, and the model achieves state-of-the-art, outperforming existing methods by a large margin on three datasets. Our code, pretrained model, and datasets are available at https://github.com/lsvih/AtTGen.
%R 10.18653/v1/2023.acl-long.119
%U https://aclanthology.org/2023.acl-long.119
%U https://doi.org/10.18653/v1/2023.acl-long.119
%P 2139-2152

Markdown (Informal)

[AtTGen: Attribute Tree Generation for Real-World Attribute Joint Extraction](https://aclanthology.org/2023.acl-long.119) (Li et al., ACL 2023)

• AtTGen: Attribute Tree Generation for Real-World Attribute Joint Extraction (Li et al., ACL 2023)

ACL

• Yanzeng Li, Bingcong Xue, Ruoyu Zhang, and Lei Zou. 2023. AtTGen: Attribute Tree Generation for Real-World Attribute Joint Extraction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2139–2152, Toronto, Canada. Association for Computational Linguistics.