{"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/a-comparison-of-neural-models-for-word","title":"A Comparison of Neural Models for Word Ordering","arxiv_id":"1708.01809","date":"2017-08-05","proceeding":"WS 2017 9","authors":["Eva Hasler","Felix Stahlberg","Marcus Tomalin","Adri`a de Gispert","Bill Byrne"],"abstract":"We compare several language models for the word-ordering task and propose a\nnew bag-to-sequence neural model based on attention-based sequence-to-sequence\nmodels. We evaluate the model on a large German WMT data set where it\nsignificantly outperforms existing models. We also describe a novel search\nstrategy for LM-based word ordering and report results on the English Penn\nTreebank. Our best model setup outperforms prior work both in terms of speed\nand quality.","url_abs":"http://arxiv.org/abs/1708.01809v1","url_pdf":"http://arxiv.org/pdf/1708.01809v1.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":"a-comparison-of-neural-models-for-word","repo_url":"https://github.com/ehasler/tensorflow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}