@inproceedings{lalitha-devi-etal-2026-lightweight,
title = "Lightweight Multilingual Coreference Resolution without {LLM}s @{CRAC}2026",
author = "Lalitha Devi, Sobha and
S, Aashik Ali and
P, Gopinath and
Sundar Ram, Vijay and
Rk Rao, Pattabhi",
editor = "Braud, Chlo{\'e} and
Hardmeier, Christian and
Ogrodniczuk, Maciej and
Loaiciga, Sharid and
Zeldes, Amir and
Nov{\'a}k, Michal and
Li, Chuyuan and
Strube, Michael and
Li, Junyi Jessy",
booktitle = "Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference ({CODI}-{CRAC} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.codi-1.26/",
pages = "199--204",
ISBN = "979-8-89176-400-2",
abstract = "This paper describes our multilingual coreference system developed for the CRAC 2026 unconstrained track. We introduce a unified, single-model architecture based on Conditional Random Fields (CRFs) that supports 20 languages. Notably, our approach achieves multilingual resolution without the use of large language models (LLMs) or pretrained weights. In contrast to resource-intensive neural methods, the proposed model is efficient, and suitable for deployment on standard hardware (CPUs). It uses linguistic and contextual features to capture coreference relations across languages with diverse syntactic and morphological properties. Model training was conducted using the official data distributions released for the CRAC 2026 shared task. This methodology provides a robust, scalable solution for multilingual NLP, demonstrating high utility within resource-constrained environments. The results highlight that feature-driven structured models remain effective for complex cross-lingual tasks. The performance on test data is similar to the results obtained for the development data."
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%0 Conference Proceedings
%T Lightweight Multilingual Coreference Resolution without LLMs @CRAC2026
%A Lalitha Devi, Sobha
%A S, Aashik Ali
%A P, Gopinath
%A Sundar Ram, Vijay
%A Rk Rao, Pattabhi
%Y Braud, Chloé
%Y Hardmeier, Christian
%Y Ogrodniczuk, Maciej
%Y Loaiciga, Sharid
%Y Zeldes, Amir
%Y Novák, Michal
%Y Li, Chuyuan
%Y Strube, Michael
%Y Li, Junyi Jessy
%S Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference (CODI-CRAC 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-400-2
%F lalitha-devi-etal-2026-lightweight
%X This paper describes our multilingual coreference system developed for the CRAC 2026 unconstrained track. We introduce a unified, single-model architecture based on Conditional Random Fields (CRFs) that supports 20 languages. Notably, our approach achieves multilingual resolution without the use of large language models (LLMs) or pretrained weights. In contrast to resource-intensive neural methods, the proposed model is efficient, and suitable for deployment on standard hardware (CPUs). It uses linguistic and contextual features to capture coreference relations across languages with diverse syntactic and morphological properties. Model training was conducted using the official data distributions released for the CRAC 2026 shared task. This methodology provides a robust, scalable solution for multilingual NLP, demonstrating high utility within resource-constrained environments. The results highlight that feature-driven structured models remain effective for complex cross-lingual tasks. The performance on test data is similar to the results obtained for the development data.
%U https://aclanthology.org/2026.codi-1.26/
%P 199-204
Markdown (Informal)
[Lightweight Multilingual Coreference Resolution without LLMs @CRAC2026](https://aclanthology.org/2026.codi-1.26/) (Lalitha Devi et al., CODI-CRAC 2026)
ACL
- Sobha Lalitha Devi, Aashik Ali S, Gopinath P, Vijay Sundar Ram, and Pattabhi Rk Rao. 2026. Lightweight Multilingual Coreference Resolution without LLMs @CRAC2026. In Proceedings of the 2nd Joint Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences and Computational Models of Reference, Anaphora and Coreference (CODI-CRAC 2026), pages 199–204, San Diego, California, USA. Association for Computational Linguistics.