@inproceedings{vuong-etal-2025-hicot,
title = "{H}i{COT}: Improving Neural Topic Models via Optimal Transport and Contrastive Learning",
author = "Vuong, Hoang Tran and
Le, Tue and
Vu, Tu and
Nguyen, Tung and
Van, Linh Ngo and
Dinh, Sang and
Nguyen, Thien Huu",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.715/",
doi = "10.18653/v1/2025.findings-acl.715",
pages = "13894--13920",
ISBN = "979-8-89176-256-5",
abstract = "Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models (PLMs), and limited modeling of hierarchical topic structures. To address these issues, we introduce HiCOT (Hierarchical Clustering and Contrastive Learning with Optimal Transport for Neural Topic Modeling), a novel framework that enhances topic coherence and efficiency. HiCOT integrates Optimal Transport to refine document-topic relationships using compact PLM-based embeddings, captures semantic structure of the documents. Additionally, it employs hierarchical clustering combine with contrastive learning to disentangle topic-word and topic-topic relationships, ensuring clearer structure and better coherence. Experimental results on multiple benchmark datasets demonstrate HiCOT{'}s superior effectiveness over existing NTMs in topic coherence, topic performance, representation quality, and computational efficiency."
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<abstract>Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models (PLMs), and limited modeling of hierarchical topic structures. To address these issues, we introduce HiCOT (Hierarchical Clustering and Contrastive Learning with Optimal Transport for Neural Topic Modeling), a novel framework that enhances topic coherence and efficiency. HiCOT integrates Optimal Transport to refine document-topic relationships using compact PLM-based embeddings, captures semantic structure of the documents. Additionally, it employs hierarchical clustering combine with contrastive learning to disentangle topic-word and topic-topic relationships, ensuring clearer structure and better coherence. Experimental results on multiple benchmark datasets demonstrate HiCOT’s superior effectiveness over existing NTMs in topic coherence, topic performance, representation quality, and computational efficiency.</abstract>
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%0 Conference Proceedings
%T HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning
%A Vuong, Hoang Tran
%A Le, Tue
%A Vu, Tu
%A Nguyen, Tung
%A Van, Linh Ngo
%A Dinh, Sang
%A Nguyen, Thien Huu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F vuong-etal-2025-hicot
%X Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models (PLMs), and limited modeling of hierarchical topic structures. To address these issues, we introduce HiCOT (Hierarchical Clustering and Contrastive Learning with Optimal Transport for Neural Topic Modeling), a novel framework that enhances topic coherence and efficiency. HiCOT integrates Optimal Transport to refine document-topic relationships using compact PLM-based embeddings, captures semantic structure of the documents. Additionally, it employs hierarchical clustering combine with contrastive learning to disentangle topic-word and topic-topic relationships, ensuring clearer structure and better coherence. Experimental results on multiple benchmark datasets demonstrate HiCOT’s superior effectiveness over existing NTMs in topic coherence, topic performance, representation quality, and computational efficiency.
%R 10.18653/v1/2025.findings-acl.715
%U https://aclanthology.org/2025.findings-acl.715/
%U https://doi.org/10.18653/v1/2025.findings-acl.715
%P 13894-13920
Markdown (Informal)
[HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning](https://aclanthology.org/2025.findings-acl.715/) (Vuong et al., Findings 2025)
ACL