{"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/bilbowa-fast-bilingual-distributed","title":"BilBOWA: Fast Bilingual Distributed Representations without Word Alignments","arxiv_id":"1410.2455","date":"2014-10-09","proceeding":null,"authors":["Stephan Gouws","Yoshua Bengio","Greg Corrado"],"abstract":"We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple\nand computationally-efficient model for learning bilingual distributed\nrepresentations of words which can scale to large monolingual datasets and does\nnot require word-aligned parallel training data. Instead it trains directly on\nmonolingual data and extracts a bilingual signal from a smaller set of raw-text\nsentence-aligned data. This is achieved using a novel sampled bag-of-words\ncross-lingual objective, which is used to regularize two noise-contrastive\nlanguage models for efficient cross-lingual feature learning. We show that\nbilingual embeddings learned using the proposed model outperform\nstate-of-the-art methods on a cross-lingual document classification task as\nwell as a lexical translation task on WMT11 data.","url_abs":"http://arxiv.org/abs/1410.2455v3","url_pdf":"http://arxiv.org/pdf/1410.2455v3.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":"bilbowa-fast-bilingual-distributed","repo_url":"https://github.com/gouwsmeister/bilbowa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"bilbowa-fast-bilingual-distributed","repo_url":"https://github.com/eske/multivec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"cross-lingual-document-classification","task_name":"Cross-Lingual Document Classification"},{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/document-classification-on-reuters-de-en","task":"Document Classification","dataset":"Reuters De-En","model":"BilBOWA","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"75"},"uses_additional_data":false},{"leaderboard":"/sota/document-classification-on-reuters-en-de","task":"Document Classification","dataset":"Reuters En-De","model":"BilBOWA","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"86.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1410.2455","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}