Summary of Zero-shot Cross-lingual Ner Using Phonemic Representations For Low-resource Languages, by Jimin Sohn et al.
Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages
by Jimin Sohn, Haeji Jung, Alex Cheng, Jooeon Kang, Yilin Du, David R. Mortensen
First submitted to arxiv on: 23 Jun 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 proposed approach to zero-shot cross-lingual named entity recognition (NER) uses phonemic representation based on the International Phonetic Alphabet (IPA) to bridge the gap between representations of different languages. This method significantly outperforms baseline models in extremely low-resource languages, with an average F1 score of 46.38% and a low standard deviation of 12.67%. The results demonstrate robustness with non-Latin scripts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Existing approaches to zero-shot cross-lingual NER require prior knowledge of the target language, which is impractical for low-resource languages. A new method uses phonemic representation based on IPA to bridge language gaps. This approach outperforms baseline models in low-resource languages, with a high average F1 score and low standard deviation. |
Keywords
» Artificial intelligence » F1 score » Named entity recognition » Ner » Zero shot
