@inproceedings{min-etal-2026-ducei,
title = "{D}u{CEI}: When to Introduce Metadata in Cross-Entity Interaction for Entity Disambiguation",
author = "Min, Jinwoo and
Nyange, Roseline and
Lee, Wonkee and
Song, Hyun-Je and
Na, Seung-Hoon",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1298/",
pages = "27453--27472",
ISBN = "979-8-89176-462-0",
abstract = "Entity disambiguation links mentions in text to the entities they refer to in a knowledge base. Recent cross-entity interaction models improve candidate comparison by jointly encoding multiple candidates, but incorporating entity metadata into this joint encoding is not straightforward. Appending metadata to every candidate forces joint encoding to process longer inputs with detailed distractor information, which can weaken the contrastive cues that make cross-entity interaction effective. We propose DuCEI, a dual-stage framework that avoids this broad metadata interaction by reducing the candidate set before introducing entity metadata. DuCEI first compares all candidates using title-based cross-entity interaction, and then performs metadata-enriched interaction only among the remaining candidates. This design makes metadata use selective at the candidate level, applying detailed evidence only where it is likely to help. Experiments on six standard benchmarks show that DuCEI achieves the best overall performance among models trained solely on AIDA-CoNLL. Further analyses show that confidence-based selective refinement preserves nearly all of the full two-stage performance while applying Stage 2 to only 27.80{\%} of mentions, reducing inference cost from 1.90{\texttimes} to approximately 1.27{\texttimes} that of Stage 1 alone. Our code is available at https://github.com/jinwoomin/DuCEI."
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<abstract>Entity disambiguation links mentions in text to the entities they refer to in a knowledge base. Recent cross-entity interaction models improve candidate comparison by jointly encoding multiple candidates, but incorporating entity metadata into this joint encoding is not straightforward. Appending metadata to every candidate forces joint encoding to process longer inputs with detailed distractor information, which can weaken the contrastive cues that make cross-entity interaction effective. We propose DuCEI, a dual-stage framework that avoids this broad metadata interaction by reducing the candidate set before introducing entity metadata. DuCEI first compares all candidates using title-based cross-entity interaction, and then performs metadata-enriched interaction only among the remaining candidates. This design makes metadata use selective at the candidate level, applying detailed evidence only where it is likely to help. Experiments on six standard benchmarks show that DuCEI achieves the best overall performance among models trained solely on AIDA-CoNLL. Further analyses show that confidence-based selective refinement preserves nearly all of the full two-stage performance while applying Stage 2 to only 27.80% of mentions, reducing inference cost from 1.90× to approximately 1.27× that of Stage 1 alone. Our code is available at https://github.com/jinwoomin/DuCEI.</abstract>
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%0 Conference Proceedings
%T DuCEI: When to Introduce Metadata in Cross-Entity Interaction for Entity Disambiguation
%A Min, Jinwoo
%A Nyange, Roseline
%A Lee, Wonkee
%A Song, Hyun-Je
%A Na, Seung-Hoon
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F min-etal-2026-ducei
%X Entity disambiguation links mentions in text to the entities they refer to in a knowledge base. Recent cross-entity interaction models improve candidate comparison by jointly encoding multiple candidates, but incorporating entity metadata into this joint encoding is not straightforward. Appending metadata to every candidate forces joint encoding to process longer inputs with detailed distractor information, which can weaken the contrastive cues that make cross-entity interaction effective. We propose DuCEI, a dual-stage framework that avoids this broad metadata interaction by reducing the candidate set before introducing entity metadata. DuCEI first compares all candidates using title-based cross-entity interaction, and then performs metadata-enriched interaction only among the remaining candidates. This design makes metadata use selective at the candidate level, applying detailed evidence only where it is likely to help. Experiments on six standard benchmarks show that DuCEI achieves the best overall performance among models trained solely on AIDA-CoNLL. Further analyses show that confidence-based selective refinement preserves nearly all of the full two-stage performance while applying Stage 2 to only 27.80% of mentions, reducing inference cost from 1.90× to approximately 1.27× that of Stage 1 alone. Our code is available at https://github.com/jinwoomin/DuCEI.
%U https://aclanthology.org/2026.findings-emnlp.1298/
%P 27453-27472
Markdown (Informal)
[DuCEI: When to Introduce Metadata in Cross-Entity Interaction for Entity Disambiguation](https://aclanthology.org/2026.findings-emnlp.1298/) (Min et al., Findings 2026)
ACL