Papers by Duc Anh Nguyen

3 papers
XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments (2025.findings-emnlp)

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Challenge: XTRA aims to uncover shared semantic themes across languages . previous methods have achieved improvements in topic diversity but struggle to ensure high topic coherence and consistent alignment across languages.
Approach: a new framework unifies Bag-of-Words modeling with multilingual embeddings is proposed to address this problem . XTRA introduces two core components: (1) representation alignment and (2) topic alignment to enforce cross-lingual consistency.
Outcome: XTRA outperforms baselines in topic coherence, diversity, and alignment quality on multilingual corpora.
GloCOM: A Short Text Neural Topic Model via Global Clustering Context (2025.naacl-long)

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Challenge: Existing neural topic models often overlook uncovering hidden topics from short texts due to data sparsity, poor aggregation quality, and difficulty in inferring topic proportions for individual documents.
Approach: They propose a model which constructs global clustering contexts for short texts using text embeddings from pre-trained language models.
Outcome: The proposed model outperforms state-of-the-art models on short texts in topic quality and document representation.
Sharpness-Aware Minimization for Topic Models with High-Quality Document Representations (2025.naacl-long)

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Challenge: Recent advances in topic models have significantly enhanced the performance compared to conventional probabilistic approaches.
Approach: They propose to use a topic model optimization method to improve generalization and performance.
Outcome: The proposed method improves topic models on popular benchmark datasets.

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