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Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models

Paper · 2026 · Thanh Vinh Nguyen, Ngo Van Dong, Minh Chu Xuan et al.

Summary

Dynamic topic models track how themes in timestamped collections appear, change and fade. The authors identify three weaknesses in current approaches. Encoders learn only from bag-of-words counts and ignore knowledge in pre-trained language models. Topics in neighbouring time slices are usually linked one-to-one, so splits and merges are missed. Topic interpretation relies on noisy top-word lists.

Their model, L-DNTM, is a VAE-based framework with three additions:

  • Distillation: PLM semantics are transferred into the encoder, both per document and at the level of latent-space geometry.
  • Optimal transport: an entropy-regularized OT term allows many-to-many matching of whole topic sets between consecutive slices.
  • LLM refinement: a KL-based step uses an LLM to sharpen topic-word distributions.

OT and LLM guidance are switched on only after a warm-up phase. Evaluated on NeurIPS, ACL, NYT, UN and WHO corpora, L-DNTM beats classical, neural and contrastive baselines on coherence, diversity and temporal coherence. It also avoids over-smoothed, stagnant topics. This matters because temporally faithful topics give more trustworthy analyses of discourse change.

Key points

  • Baselines: DTM, NDTM, NDTM-b, DETM, BERTopic, DSNTM and CFDTM. L-DNTM has the best Temporal Topic Coherence (TTC) on every dataset in the temporal evaluation, with TTC between roughly 0.16 and 0.20.
  • Static topic quality: L-DNTM reaches TC of 0.594 (NeurIPS), 0.554 (ACL) and 0.629 (NYT), with TD of 0.841, 0.853 and 0.862 respectively. This exceeds CFDTM on both metrics.
  • Temporal Topic Smoothness (TTS): L-DNTM's moderate scores (about 0.555–0.629) are treated as desirable. Very high TTS, as seen in several baselines, signals stagnant topics.
  • Ablations (NYT, NeurIPS):
    • Removing distillation or OT lowers both TQ and TTC.
    • Removing the LLM component slightly raises TQ and TTC but pushes TTS to about 0.93–0.94, meaning topics stop evolving.
  • Limitation and downstream use:
    • On WHO health reports, BERTopic wins all metrics, which the authors attribute to repetitive, low-variation text.
    • Document-topic vectors are also tested on NYT through SVM classification (Accuracy, F1) and clustering (Purity, NMI).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Monash UniversityMelbourne · Australia60 works · 54,866 citations🎓Hanoi University of Science and TechnologyHanoi · Vietnam10 works · 89 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksGeneral Social Sciences · 227 works

🧭 Topics 1Research subjects and languages the work is about.Interpretability · 130 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Topic modeling · 47 works

Cite

Cite as Thanh Vinh Nguyen, Ngo Van Dong, Minh Chu Xuan, Tung Nguyen, Linh Ngo Van, Dinh Viet Sang and Trung Le. 2026. Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-eacl.187

@inproceedings{nguyen2026beyond,
  title = {{Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models}},
  author = {Thanh Vinh Nguyen, Ngo {Van Dong}, Minh Chu Xuan, Tung Nguyen, Linh Ngo Van, Dinh Viet Sang and Trung Le},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.187},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.187}
}

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