@inproceedings{li-etal-2026-role,
title = "On the Role of Discriminative Models in Generative Relation Extraction",
author = "Li, Guozheng and
Wang, Peng and
Xu, Zijie and
Zhou, Jing and
Liu, Jiajun and
Shang, Ziyu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2093/",
pages = "45167--45183",
ISBN = "979-8-89176-390-6",
abstract = "Relation extraction (RE) identifies semantic relations between entities in text, with existing methods falling into two main paradigms: discriminative and generative. Discriminative models encode sentences and entities into relation representations and classify the most likely relation, whereas generative models directly produce relation labels through sequence generation. Although the latter have benefited from recent advances in large language models (LLMs), their performance remains limited by bottlenecks. In this work, we present the systematic investigation of how discriminative models can support generative RE. We propose the Discriminative-to-Generative (D2G) framework, which first leverages discriminative models to produce a top-k set of candidate relations, and then integrates this knowledge into generative models via in-context or prompt learning. Extensive experiments on five widely used RE benchmarks demonstrate that D2G consistently achieves state-of-the-art performance, with notable gains on long-tailed relation classes."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2026-role">
<titleInfo>
<title>On the Role of Discriminative Models in Generative Relation Extraction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Guozheng</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Peng</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zijie</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jing</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ziyu</namePart>
<namePart type="family">Shang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-390-6</identifier>
</relatedItem>
<abstract>Relation extraction (RE) identifies semantic relations between entities in text, with existing methods falling into two main paradigms: discriminative and generative. Discriminative models encode sentences and entities into relation representations and classify the most likely relation, whereas generative models directly produce relation labels through sequence generation. Although the latter have benefited from recent advances in large language models (LLMs), their performance remains limited by bottlenecks. In this work, we present the systematic investigation of how discriminative models can support generative RE. We propose the Discriminative-to-Generative (D2G) framework, which first leverages discriminative models to produce a top-k set of candidate relations, and then integrates this knowledge into generative models via in-context or prompt learning. Extensive experiments on five widely used RE benchmarks demonstrate that D2G consistently achieves state-of-the-art performance, with notable gains on long-tailed relation classes.</abstract>
<identifier type="citekey">li-etal-2026-role</identifier>
<location>
<url>https://aclanthology.org/2026.acl-long.2093/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>45167</start>
<end>45183</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T On the Role of Discriminative Models in Generative Relation Extraction
%A Li, Guozheng
%A Wang, Peng
%A Xu, Zijie
%A Zhou, Jing
%A Liu, Jiajun
%A Shang, Ziyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-role
%X Relation extraction (RE) identifies semantic relations between entities in text, with existing methods falling into two main paradigms: discriminative and generative. Discriminative models encode sentences and entities into relation representations and classify the most likely relation, whereas generative models directly produce relation labels through sequence generation. Although the latter have benefited from recent advances in large language models (LLMs), their performance remains limited by bottlenecks. In this work, we present the systematic investigation of how discriminative models can support generative RE. We propose the Discriminative-to-Generative (D2G) framework, which first leverages discriminative models to produce a top-k set of candidate relations, and then integrates this knowledge into generative models via in-context or prompt learning. Extensive experiments on five widely used RE benchmarks demonstrate that D2G consistently achieves state-of-the-art performance, with notable gains on long-tailed relation classes.
%U https://aclanthology.org/2026.acl-long.2093/
%P 45167-45183
Markdown (Informal)
[On the Role of Discriminative Models in Generative Relation Extraction](https://aclanthology.org/2026.acl-long.2093/) (Li et al., ACL 2026)
ACL
- Guozheng Li, Peng Wang, Zijie Xu, Jing Zhou, Jiajun Liu, and Ziyu Shang. 2026. On the Role of Discriminative Models in Generative Relation Extraction. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45167–45183, San Diego, California, United States. Association for Computational Linguistics.