Summary of Universal Cross-lingual Text Classification, by Riya Savant et al.
Universal Cross-Lingual Text Classification
by Riya Savant, Anushka Shelke, Sakshi Todmal, Sanskruti Kanphade, Ananya Joshi, Raviraj Joshi
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 research aims to optimize existing labels and datasets in different languages to create a unified model for Universal Cross-Lingual Text Classification. By blending supervised data from various languages during training, the approach seeks to enhance label and language coverage, ultimately achieving a label set that represents the union of labels from multiple languages. The study utilizes a strong multilingual SBERT as the base model, enabling the novel training strategy and its adaptability in cross-lingual language transfer scenarios. This research explores methodologies and implications for developing a robust and adaptable universal cross-lingual model, particularly focusing on low-resource languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to classify text into different categories across many languages using machine learning. It’s hard to find labeled data for languages that don’t have much written content, making it difficult to train models that can understand these languages well. The researchers propose a new way to train a model that works across languages by combining labeled data from multiple languages during training. This approach aims to improve the coverage of labels and languages, allowing the model to classify text in languages it hasn’t seen before. |
Keywords
» Artificial intelligence » Machine learning » Supervised » Text classification
