{"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/neural-cross-lingual-named-entity-recognition","title":"Neural Cross-Lingual Named Entity Recognition with Minimal Resources","arxiv_id":"1808.09861","date":"2018-08-29","proceeding":"EMNLP 2018 10","authors":["Jiateng Xie","Zhilin Yang","Graham Neubig","Noah A. Smith","Jaime Carbonell"],"abstract":"For languages with no annotated resources, unsupervised transfer of natural\nlanguage processing models such as named-entity recognition (NER) from\nresource-rich languages would be an appealing capability. However, differences\nin words and word order across languages make it a challenging problem. To\nimprove mapping of lexical items across languages, we propose a method that\nfinds translations based on bilingual word embeddings. To improve robustness to\nword order differences, we propose to use self-attention, which allows for a\ndegree of flexibility with respect to word order. We demonstrate that these\nmethods achieve state-of-the-art or competitive NER performance on commonly\ntested languages under a cross-lingual setting, with much lower resource\nrequirements than past approaches. We also evaluate the challenges of applying\nthese methods to Uyghur, a low-resource language.","url_abs":"http://arxiv.org/abs/1808.09861v2","url_pdf":"http://arxiv.org/pdf/1808.09861v2.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":"neural-cross-lingual-named-entity-recognition","repo_url":"https://github.com/thespectrewithin/cross-lingual_NER","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1808.09861","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}