Summary of A New Method For Cross-lingual-based Semantic Role Labeling, by Mohammad Ebrahimi et al.
A New Method for Cross-Lingual-based Semantic Role Labeling
by Mohammad Ebrahimi, Behrouz Minaei Bidgoli, Nasim Khozouei
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 deep learning algorithm based on model transfer addresses the challenge of limited annotated data in multiple languages for semantic role labeling (SRL). The model uses a dataset combining English CoNLL2009 and Persian SRL corpus, with only 10% educational data used to optimize training. Compared to Niksirt et al.’s model, the proposed model shows significant improvements: 2.05% F1-score gain in monolingual mode and 6.23% in cross-lingual mode. The compared model’s limitations suggest that the actual superiority of the proposed model is even greater. This development paves the way for further research in understanding and processing natural language across different linguistic contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new approach to help computers better understand language. Right now, there isn’t enough labeled data to train models for many languages. To fix this, the researchers created a deep learning algorithm that can transfer knowledge from one language to another. They used English and Persian data to test their model, which did much better than previous attempts. This breakthrough has huge potential for understanding and processing language across different cultures. |
Keywords
» Artificial intelligence » Deep learning » F1 score
