{"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/towards-cross-lingual-distributed","title":"Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders","arxiv_id":"1608.02996","date":"2016-08-09","proceeding":"WS 2016 8","authors":["Antonio Valerio Miceli Barone"],"abstract":"Current approaches to learning vector representations of text that are\ncompatible between different languages usually require some amount of parallel\ntext, aligned at word, sentence or at least document level. We hypothesize\nhowever, that different natural languages share enough semantic structure that\nit should be possible, in principle, to learn compatible vector representations\njust by analyzing the monolingual distribution of words.\n  In order to evaluate this hypothesis, we propose a scheme to map word vectors\ntrained on a source language to vectors semantically compatible with word\nvectors trained on a target language using an adversarial autoencoder.\n  We present preliminary qualitative results and discuss possible future\ndevelopments of this technique, such as applications to cross-lingual sentence\nrepresentations.","url_abs":"http://arxiv.org/abs/1608.02996v1","url_pdf":"http://arxiv.org/pdf/1608.02996v1.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":"towards-cross-lingual-distributed","repo_url":"https://github.com/Avmb/clweadv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.02996","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}