@inproceedings{nguyen-etal-2026-dont,
title = "Don{'}t Generate, Just Score: Enhancing Topic Coherence with Masked Semantic Alignment",
author = "Nguyen, Tien-Phat and
Xuan, Minh Chu and
Huy, Phung Gia and
Van, Linh Ngo and
Nguyen, Duc Anh and
Le, Trung and
Nguyen, Thien Huu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.226/",
pages = "4739--4766",
ISBN = "979-8-89176-462-0",
abstract = "Neural topic models are widely used to uncover latent thematic structure in text corpora, but often produce topics with limited semantic coherence and weak alignment between topics and documents. Recent refinement approaches based on large language models (LLMs) have shown promising improvements, yet typically rely on repeated generative inference, resulting in high computational cost and practical issues such as sensitivity to prompting, stochastic variability, and the introduction of out-of-vocabulary content. We propose BERT-NTM, an encoder-based semantic refinement framework for neural topic modeling that avoids generative inference altogether. Instead of generating or rewriting topic words, BERT-NTM leverages a pretrained BERT encoder to provide discriminative semantic signals during a refinement phase: topic{--}word distributions are refined by scoring candidate words within a topic using masked language modeling, and document{--}topic representations are improved by aligning document embeddings with mixtures of topic embeddings via a Maximum Mean Discrepancy (MMD) objective. Both refinement components are fully parallelizable, stable, and operate strictly within the model vocabulary. Experiments on standard topic modeling benchmarks demonstrate that BERT-NTM consistently improves topic coherence and document{--}topic alignment while substantially reducing runtime and GPU memory usage compared to LLM-based refinement methods."
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<abstract>Neural topic models are widely used to uncover latent thematic structure in text corpora, but often produce topics with limited semantic coherence and weak alignment between topics and documents. Recent refinement approaches based on large language models (LLMs) have shown promising improvements, yet typically rely on repeated generative inference, resulting in high computational cost and practical issues such as sensitivity to prompting, stochastic variability, and the introduction of out-of-vocabulary content. We propose BERT-NTM, an encoder-based semantic refinement framework for neural topic modeling that avoids generative inference altogether. Instead of generating or rewriting topic words, BERT-NTM leverages a pretrained BERT encoder to provide discriminative semantic signals during a refinement phase: topic–word distributions are refined by scoring candidate words within a topic using masked language modeling, and document–topic representations are improved by aligning document embeddings with mixtures of topic embeddings via a Maximum Mean Discrepancy (MMD) objective. Both refinement components are fully parallelizable, stable, and operate strictly within the model vocabulary. Experiments on standard topic modeling benchmarks demonstrate that BERT-NTM consistently improves topic coherence and document–topic alignment while substantially reducing runtime and GPU memory usage compared to LLM-based refinement methods.</abstract>
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%0 Conference Proceedings
%T Don’t Generate, Just Score: Enhancing Topic Coherence with Masked Semantic Alignment
%A Nguyen, Tien-Phat
%A Xuan, Minh Chu
%A Huy, Phung Gia
%A Van, Linh Ngo
%A Nguyen, Duc Anh
%A Le, Trung
%A Nguyen, Thien Huu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F nguyen-etal-2026-dont
%X Neural topic models are widely used to uncover latent thematic structure in text corpora, but often produce topics with limited semantic coherence and weak alignment between topics and documents. Recent refinement approaches based on large language models (LLMs) have shown promising improvements, yet typically rely on repeated generative inference, resulting in high computational cost and practical issues such as sensitivity to prompting, stochastic variability, and the introduction of out-of-vocabulary content. We propose BERT-NTM, an encoder-based semantic refinement framework for neural topic modeling that avoids generative inference altogether. Instead of generating or rewriting topic words, BERT-NTM leverages a pretrained BERT encoder to provide discriminative semantic signals during a refinement phase: topic–word distributions are refined by scoring candidate words within a topic using masked language modeling, and document–topic representations are improved by aligning document embeddings with mixtures of topic embeddings via a Maximum Mean Discrepancy (MMD) objective. Both refinement components are fully parallelizable, stable, and operate strictly within the model vocabulary. Experiments on standard topic modeling benchmarks demonstrate that BERT-NTM consistently improves topic coherence and document–topic alignment while substantially reducing runtime and GPU memory usage compared to LLM-based refinement methods.
%U https://aclanthology.org/2026.findings-emnlp.226/
%P 4739-4766
Markdown (Informal)
[Don’t Generate, Just Score: Enhancing Topic Coherence with Masked Semantic Alignment](https://aclanthology.org/2026.findings-emnlp.226/) (Nguyen et al., Findings 2026)
ACL
- Tien-Phat Nguyen, Minh Chu Xuan, Phung Gia Huy, Linh Ngo Van, Duc Anh Nguyen, Trung Le, and Thien Huu Nguyen. 2026. Don’t Generate, Just Score: Enhancing Topic Coherence with Masked Semantic Alignment. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 4739–4766, Budapest, Hungary. Association for Computational Linguistics.