@inproceedings{nguyen-etal-2026-beyond,
title = "Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models",
author = "Nguyen, Thanh Vinh and
Van Dong, Ngo and
Xuan, Minh Chu and
Nguyen, Tung and
Van, Linh Ngo and
Sang, Dinh Viet and
Le, Trung",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.187/",
pages = "3609--3629",
ISBN = "979-8-89176-386-9",
abstract = "Dynamic topic models aim to reveal how themes emerge, evolve, and dissolve in time-stamped corpora, but existing approaches still face three major challenges: (i) encoders capture bag-of-words statistics but fail to align with the rich semantic priors of large pre-trained language models, (ii) temporal linkages are often modeled as rigid one-to-one chains, limiting the ability to track non-linear evolution such as topic splits or merges, and (iii) interpretability remains shallow, relying on noisy top-word lists that obscure thematic clarity. We propose L-DNTM (LLM-Augmented for Dynamic Neural Topic Model), a variational framework designed to capture more faithful temporal trajectories. Our model integrates three key components: multi-objective distillation to inject PLM-derived semantic knowledge into the encoder, entropy-regularized optimal transport to align entire topic constellations across time for smooth yet flexible evolution, and LLM-guided refinement to sharpen topic{--}word distributions for improved interpretability. Extensive experiments on diverse corpora show that L-DNTM yields more coherent, temporally consistent, and interpretable topic dynamics, and further enhances downstream classification and clustering tasks."
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<abstract>Dynamic topic models aim to reveal how themes emerge, evolve, and dissolve in time-stamped corpora, but existing approaches still face three major challenges: (i) encoders capture bag-of-words statistics but fail to align with the rich semantic priors of large pre-trained language models, (ii) temporal linkages are often modeled as rigid one-to-one chains, limiting the ability to track non-linear evolution such as topic splits or merges, and (iii) interpretability remains shallow, relying on noisy top-word lists that obscure thematic clarity. We propose L-DNTM (LLM-Augmented for Dynamic Neural Topic Model), a variational framework designed to capture more faithful temporal trajectories. Our model integrates three key components: multi-objective distillation to inject PLM-derived semantic knowledge into the encoder, entropy-regularized optimal transport to align entire topic constellations across time for smooth yet flexible evolution, and LLM-guided refinement to sharpen topic–word distributions for improved interpretability. Extensive experiments on diverse corpora show that L-DNTM yields more coherent, temporally consistent, and interpretable topic dynamics, and further enhances downstream classification and clustering tasks.</abstract>
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%0 Conference Proceedings
%T Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models
%A Nguyen, Thanh Vinh
%A Van Dong, Ngo
%A Xuan, Minh Chu
%A Nguyen, Tung
%A Van, Linh Ngo
%A Sang, Dinh Viet
%A Le, Trung
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F nguyen-etal-2026-beyond
%X Dynamic topic models aim to reveal how themes emerge, evolve, and dissolve in time-stamped corpora, but existing approaches still face three major challenges: (i) encoders capture bag-of-words statistics but fail to align with the rich semantic priors of large pre-trained language models, (ii) temporal linkages are often modeled as rigid one-to-one chains, limiting the ability to track non-linear evolution such as topic splits or merges, and (iii) interpretability remains shallow, relying on noisy top-word lists that obscure thematic clarity. We propose L-DNTM (LLM-Augmented for Dynamic Neural Topic Model), a variational framework designed to capture more faithful temporal trajectories. Our model integrates three key components: multi-objective distillation to inject PLM-derived semantic knowledge into the encoder, entropy-regularized optimal transport to align entire topic constellations across time for smooth yet flexible evolution, and LLM-guided refinement to sharpen topic–word distributions for improved interpretability. Extensive experiments on diverse corpora show that L-DNTM yields more coherent, temporally consistent, and interpretable topic dynamics, and further enhances downstream classification and clustering tasks.
%U https://aclanthology.org/2026.findings-eacl.187/
%P 3609-3629
Markdown (Informal)
[Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models](https://aclanthology.org/2026.findings-eacl.187/) (Nguyen et al., Findings 2026)
ACL