Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages
This addresses the challenge of NER for low-resource languages where prior knowledge is limited, offering a practical solution for language processing in such contexts.
The paper tackles the problem of zero-shot cross-lingual named entity recognition (NER) for low-resource languages by proposing a method using phonemic representations based on the International Phonetic Alphabet (IPA), achieving a highest average F1 score of 46.38% and demonstrating robustness with non-Latin scripts.
Existing zero-shot cross-lingual NER approaches require substantial prior knowledge of the target language, which is impractical for low-resource languages. In this paper, we propose a novel approach to NER using phonemic representation based on the International Phonetic Alphabet (IPA) to bridge the gap between representations of different languages. Our experiments show that our method significantly outperforms baseline models in extremely low-resource languages, with the highest average F1 score (46.38%) and lowest standard deviation (12.67), particularly demonstrating its robustness with non-Latin scripts. Our codes are available at https://github.com/Gabriel819/zeroshot_ner.git