@inproceedings{luo-etal-2026-meic,
title = "{MEIC}-{DT}: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints",
author = "Luo, Kangyang and
Si, Shuzheng and
Bai, Yuzhuo and
Gao, Cheng and
Wang, Zhitong and
Huang, Cheng and
Shen, Yingli and
Han, Yufeng and
Li, Wenhao and
Kong, Cunliang and
Sun, Maosong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.881/",
pages = "17787--17804",
ISBN = "979-8-89176-395-1",
abstract = "In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, particularly in incremental clustering, which faces the critical challenge of balancing efficiency with performance for long texts. To address the limitation, we propose MEIC-DT, a novel dual-threshold, memory-efficient incremental clustering approach based on a lightweight Transformer. MEIC-DT features a dual-threshold constraint mechanism designed to precisely control the Transformer{'}s input scale within a predefined memory budget. This mechanism incorporates two key components: a Statistics-Aware Eviction Strategy (SAES) and an Internal Regularization Policy (IRP). The SAES utilizes distinct statistical profiles from the training and inference phases for intelligent cache management. The IRP strategically condenses clusters by selecting the most representative mentions, thereby preserving semantic integrity. Extensive experiments on common benchmarks demonstrate that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints."
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%0 Conference Proceedings
%T MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints
%A Luo, Kangyang
%A Si, Shuzheng
%A Bai, Yuzhuo
%A Gao, Cheng
%A Wang, Zhitong
%A Huang, Cheng
%A Shen, Yingli
%A Han, Yufeng
%A Li, Wenhao
%A Kong, Cunliang
%A Sun, Maosong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F luo-etal-2026-meic
%X In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, particularly in incremental clustering, which faces the critical challenge of balancing efficiency with performance for long texts. To address the limitation, we propose MEIC-DT, a novel dual-threshold, memory-efficient incremental clustering approach based on a lightweight Transformer. MEIC-DT features a dual-threshold constraint mechanism designed to precisely control the Transformer’s input scale within a predefined memory budget. This mechanism incorporates two key components: a Statistics-Aware Eviction Strategy (SAES) and an Internal Regularization Policy (IRP). The SAES utilizes distinct statistical profiles from the training and inference phases for intelligent cache management. The IRP strategically condenses clusters by selecting the most representative mentions, thereby preserving semantic integrity. Extensive experiments on common benchmarks demonstrate that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints.
%U https://aclanthology.org/2026.findings-acl.881/
%P 17787-17804
Markdown (Informal)
[MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints](https://aclanthology.org/2026.findings-acl.881/) (Luo et al., Findings 2026)
ACL
- Kangyang Luo, Shuzheng Si, Yuzhuo Bai, Cheng Gao, Zhitong Wang, Cheng Huang, Yingli Shen, Yufeng Han, Wenhao Li, Cunliang Kong, and Maosong Sun. 2026. MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17787–17804, San Diego, California, United States. Association for Computational Linguistics.