{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/beto-bentz-becas-the-surprising-cross-lingual","title":"Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT","arxiv_id":"1904.09077","date":"2019-04-19","proceeding":"IJCNLP 2019 11","authors":["Shijie Wu","Mark Dredze"],"abstract":"Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.","url_abs":"https://arxiv.org/abs/1904.09077v2","url_pdf":"https://arxiv.org/pdf/1904.09077v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"beto-bentz-becas-the-surprising-cross-lingual","repo_url":"https://github.com/shijie-wu/crosslingual-nlp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"beto-bentz-becas-the-surprising-cross-lingual","repo_url":"https://github.com/stefan-it/fine-tuned-berts-seq","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"cross-lingual-ner","task_name":"Cross-Lingual NER"},{"task_slug":"cross-lingual-transfer","task_name":"Cross-Lingual Transfer"},{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"pos","task_name":"POS"},{"task_slug":"pos-tagging","task_name":"POS Tagging"},{"task_slug":"zero-shot-cross-lingual-transfer","task_name":"Zero-Shot Cross-Lingual Transfer"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"},{"method_slug":"mbert","method_name":"mBERT"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cross-lingual-ner-on-conll-dutch","task":"Cross-Lingual NER","dataset":"CoNLL Dutch","model":"mBERT","rank_in_archive_order":9,"of":10,"metrics":{"F1":"77.57"},"uses_additional_data":false},{"leaderboard":"/sota/cross-lingual-ner-on-conll-german","task":"Cross-Lingual NER","dataset":"CoNLL German","model":"mBERT","rank_in_archive_order":9,"of":10,"metrics":{"F1":"69.56"},"uses_additional_data":false},{"leaderboard":"/sota/cross-lingual-ner-on-conll-spanish","task":"Cross-Lingual NER","dataset":"CoNLL Spanish","model":"mBERT","rank_in_archive_order":8,"of":10,"metrics":{"F1":"74.96"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.09077","atlas_url":"https://app.syntology.ai/?focus=1904.09077","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}