Cross-lingual Effectiveness of BERT Models 🌍

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This research explores methods of evaluating the cross-lingual effectiveness of multilingual BERT models, covering key architectural aspects like training on 104 languages and fine-tuning for diverse NLP tasks. It assesses performance using tasks such as document classification, NLI, NER, POS tagging, and dependency parsing, placing emphasis on techniques like zero-shot transfer and layer freezing. The study provides insights into the impact of shared subword tokens and suggests future...