Summary of Event Extraction in Basque: Typologically Motivated Cross-lingual Transfer-learning Analysis, by Mikel Zubillaga et al.
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis
by Mikel Zubillaga, Oscar Sainz, Ainara Estarrona, Oier Lopez de Lacalle, Eneko Agirre
First submitted to arxiv on: 9 Apr 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 A novel study investigates how typological similarity between source and target languages affects the performance of cross-lingual transfer-learning in Event Extraction. A multilingual language model is trained on a source language and applied to Basque, a typologically distinct target language. Three Event Extraction tasks demonstrate that shared linguistic characteristics impact transfer quality. Further analysis across 72 language pairs reveals that token classification tasks benefit from common writing script and morphological features, while structural prediction tasks rely on word order similarity. The study also explores how training size affects cross-lingual performance, introducing the EusIE dataset for Basque and MEE. Code and datasets are publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language similarities affect machine learning models that can understand different languages. They tested this idea by using a multilingual model trained on one language to understand another language, like Basque. The results show that if the source and target languages have similar grammar or writing systems, the model performs better. This matters because it could help create more accurate language translation tools for languages with limited training data. |
Keywords
» Artificial intelligence » Classification » Language model » Machine learning » Token » Transfer learning » Translation
