{"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/cross-lingual-distillation-for-text","title":"Cross-lingual Distillation for Text Classification","arxiv_id":"1705.02073","date":"2017-05-05","proceeding":"ACL 2017 7","authors":["Ruochen Xu","Yiming Yang"],"abstract":"Cross-lingual text classification(CLTC) is the task of classifying documents\nwritten in different languages into the same taxonomy of categories. This paper\npresents a novel approach to CLTC that builds on model distillation, which\nadapts and extends a framework originally proposed for model compression. Using\nsoft probabilistic predictions for the documents in a label-rich language as\nthe (induced) supervisory labels in a parallel corpus of documents, we train\nclassifiers successfully for new languages in which labeled training data are\nnot available. An adversarial feature adaptation technique is also applied\nduring the model training to reduce distribution mismatch. We conducted\nexperiments on two benchmark CLTC datasets, treating English as the source\nlanguage and German, French, Japan and Chinese as the unlabeled target\nlanguages. The proposed approach had the advantageous or comparable performance\nof the other state-of-art methods.","url_abs":"http://arxiv.org/abs/1705.02073v2","url_pdf":"http://arxiv.org/pdf/1705.02073v2.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":"cross-lingual-distillation-for-text","repo_url":"https://github.com/xrc10/cross-distill","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"model-compression","task_name":"Model Compression"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.02073","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}