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        <title>Revisiting Pre-trained Language Models for Conversation Disentanglement</title>
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    <abstract>Multi-party conversation is a popular form in online group chatting. However, the interweaving of utterance threads complicates the understanding of the dialogues for participants. Many conversation disentanglement models have been proposed using transformer-based pre-trained language models (PrLMs). However, advanced transformer-based PrLMs have not been extensively studied. This paper investigates the effectiveness of five advanced PrLMs: BERT, XLNet, ELECTRA, RoBERTa, and ModernBERT. The experimental results show that ELECTRA and RoBERTa are two PrLMs with outstanding performance than other PrLMs for the conversation disentanglement task.</abstract>
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%0 Conference Proceedings
%T Revisiting Pre-trained Language Models for Conversation Disentanglement
%A Lam, Tung-Thien
%A Yang, Cheng-Zen
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F lam-yang-2025-revisiting
%X Multi-party conversation is a popular form in online group chatting. However, the interweaving of utterance threads complicates the understanding of the dialogues for participants. Many conversation disentanglement models have been proposed using transformer-based pre-trained language models (PrLMs). However, advanced transformer-based PrLMs have not been extensively studied. This paper investigates the effectiveness of five advanced PrLMs: BERT, XLNet, ELECTRA, RoBERTa, and ModernBERT. The experimental results show that ELECTRA and RoBERTa are two PrLMs with outstanding performance than other PrLMs for the conversation disentanglement task.
%U https://aclanthology.org/2025.rocling-main.31/
%P 296-302

Markdown (Informal)

[Revisiting Pre-trained Language Models for Conversation Disentanglement](https://aclanthology.org/2025.rocling-main.31/) (Lam & Yang, ROCLING 2025)

• Revisiting Pre-trained Language Models for Conversation Disentanglement (Lam & Yang, ROCLING 2025)

ACL

• Tung-Thien Lam and Cheng-Zen Yang. 2025. Revisiting Pre-trained Language Models for Conversation Disentanglement. In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 296–302, National Taiwan University, Taipei City, Taiwan. Association for Computational Linguistics.