Summary of Refining Dimensions For Improving Clustering-based Cross-lingual Topic Models, by Chia-hsuan Chang et al.
Refining Dimensions for Improving Clustering-based Cross-lingual Topic Models
by Chia-Hsuan Chang, Tien-Yuan Huang, Yi-Hang Tsai, Chia-Ming Chang, San-Yih Hwang
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel SVD-based dimension refinement component into the clustering-based topic model pipeline, aiming to improve cross-lingual topic identification. The existing pipeline performs well in monolingual topic identification but struggles with language-dependent dimensions (LDDs) generated by multilingual language models. By refining these LDDs, the updated pipeline can accurately identify topics across languages. Experiments on three datasets show that this approach outperforms other state-of-the-art cross-lingual topic models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves how computers understand and group ideas from different languages. It makes a special component to help with this, called SVD-based dimension refinement. This helps remove language-specific problems that make it hard for the computer to identify topics correctly. The new approach works better than other methods on three datasets, showing its effectiveness. |
Keywords
» Artificial intelligence » Clustering
