P roto XTM : Cross-Lingual Topic Modeling with Document-Level Prototype-based Contrastive Learning (2025)

Abstract: Cross-lingual topic modeling (CLTM) is an essential task in the field of data mining and natural language processing, aiming to extract aligned and semantically coherent topics from bilingual corpora. Recent advances in cross-lingual neural topic models have widely leveraged bilingual dictionaries to achieve word-level topic alignment. However, two critical challenges remain in cross-lingual topic modeling, the topic mismatch issue and the degeneration of intra-lingual topic interpretability. Due to linguistic diversity, some translated word pairs may not represent semantically coherent topics despite being lexical equivalents, and the objective of cross-lingual topic alignment in CLTM can consequently degrade topic interpretability within intra languages. To address these issues, we propose a novel document-level prototype-based contrastive learning paradigm for cross-lingual topic modeling. Additionally, we design a retrieval-based positive sampling strategy for contrastive learning without data augmentation. Furthermore, we introduce ProtoXTM, a cross-lingual neural topic model based on document-level prototype-based contrastive learning. Extensive experiments indicate that our approach achieves state-of-the-art performance on cross-lingual and mono-lingual benchmarks, demonstrating enhanced topic interpretability.

## introduction cross-lingual topic modeling (cltm) aims to discover aligned and semantically coherent structures in bilingual corpora. cltm has been widely applied in various natural language processing (nlp) tasks, including cross-lingual information retrieval @xcite , entity linking @xcite , sentiment analysis @xcite , and trend tracking @xcite ). the traditional polylingual topic model @xcite discovers aligned topics using tuplebased comparable documents in different languages. ## proposed methodology problem setting. we denote non-parallel bilingual corpus as x 1 ,x 2 on language l 1 and language l 2 , which consists of m 1 , m 2 documents ## conclusion in this paper, we identify two critical issues in cross-lingual topic modeling, the topic mismatch issue and the degeneration of intra-lingual topic interpretability. furthermore, we propose a novel cross-lingual neural topic modeling framework, protoxtm, effectively mitigates topic mismatch issue and intra-lingual topic degradation by retrievalbased positive sampling strategy and documentlevel prototype-based contrastive learning. extensive experimental results demonstrate that pro-toxtm outperforms the baseline methods in both cross-lingual and intra-lingual topic coherence, and can infer document-topic distributions with high transferability.