{"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/leveraging-monolingual-data-for-crosslingual","title":"Leveraging Monolingual Data for Crosslingual Compositional Word Representations","arxiv_id":"1412.6334","date":"2014-12-19","proceeding":null,"authors":["Hubert Soyer","Pontus Stenetorp","Akiko Aizawa"],"abstract":"In this work, we present a novel neural network based architecture for\ninducing compositional crosslingual word representations. Unlike previously\nproposed methods, our method fulfills the following three criteria; it\nconstrains the word-level representations to be compositional, it is capable of\nleveraging both bilingual and monolingual data, and it is scalable to large\nvocabularies and large quantities of data. The key component of our approach is\nwhat we refer to as a monolingual inclusion criterion, that exploits the\nobservation that phrases are more closely semantically related to their\nsub-phrases than to other randomly sampled phrases. We evaluate our method on a\nwell-established crosslingual document classification task and achieve results\nthat are either comparable, or greatly improve upon previous state-of-the-art\nmethods. Concretely, our method reaches a level of 92.7% and 84.4% accuracy for\nthe English to German and German to English sub-tasks respectively. The former\nadvances the state of the art by 0.9% points of accuracy, the latter is an\nabsolute improvement upon the previous state of the art by 7.7% points of\naccuracy and an improvement of 33.0% in error reduction.","url_abs":"http://arxiv.org/abs/1412.6334v4","url_pdf":"http://arxiv.org/pdf/1412.6334v4.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":"leveraging-monolingual-data-for-crosslingual","repo_url":"https://github.com/ogh/binclusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"document-classification","task_name":"Document Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cross-lingual-document-classification-on-12","task":"Cross-Lingual Document Classification","dataset":"Reuters RCV1/RCV2 English-to-German","model":"Biinclusion (Euro500kReuters)","rank_in_archive_order":1,"of":3,"metrics":{"Accuracy":"92.7"},"uses_additional_data":false},{"leaderboard":"/sota/cross-lingual-document-classification-on-13","task":"Cross-Lingual Document Classification","dataset":"Reuters RCV1/RCV2 German-to-English","model":"Biinclusion (Euro500kReuters)","rank_in_archive_order":1,"of":3,"metrics":{"Accuracy":"84.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}