Summary of Zero-shot Cross-lingual Transfer Learning with Multiple Source and Target Languages For Information Extraction: Language Selection and Adversarial Training, by Nghia Trung Ngo et al.
Zero-shot Cross-lingual Transfer Learning with Multiple Source and Target Languages for Information Extraction: Language Selection and Adversarial Training
by Nghia Trung Ngo, Thien Huu Nguyen
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel approach to improve cross-lingual language understanding by analyzing the correlation between single-transfer performance and linguistic-based distances. The authors develop a combined language distance metric that is highly correlated and robust across different tasks and model scales. They also investigate zero-shot multi-lingual transfer settings, where multiple languages are involved in training and evaluation processes. The paper concludes with a relational-transfer setting that incorporates multi-lingual unlabeled data using adversarial training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to develop a multi-lingual IE system that can generalize to many languages. By analyzing single-transfer performance and linguistic-based distances, the authors create a combined language distance metric that is highly correlated and robust. The paper also explores zero-shot multi-lingual transfer settings and proposes a relational-transfer setting using adversarial training. |
Keywords
» Artificial intelligence » Language understanding » Zero shot
