{"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/modeling-localness-for-self-attention","title":"Modeling Localness for Self-Attention Networks","arxiv_id":"1810.10182","date":"2018-10-24","proceeding":"EMNLP 2018 10","authors":["Baosong Yang","Zhaopeng Tu","Derek F. Wong","Fandong Meng","Lidia S. Chao","Tong Zhang"],"abstract":"Self-attention networks have proven to be of profound value for its strength\nof capturing global dependencies. In this work, we propose to model localness\nfor self-attention networks, which enhances the ability of capturing useful\nlocal context. We cast localness modeling as a learnable Gaussian bias, which\nindicates the central and scope of the local region to be paid more attention.\nThe bias is then incorporated into the original attention distribution to form\na revised distribution. To maintain the strength of capturing long distance\ndependencies and enhance the ability of capturing short-range dependencies, we\nonly apply localness modeling to lower layers of self-attention networks.\nQuantitative and qualitative analyses on Chinese-English and English-German\ntranslation tasks demonstrate the effectiveness and universality of the\nproposed approach.","url_abs":"http://arxiv.org/abs/1810.10182v1","url_pdf":"http://arxiv.org/pdf/1810.10182v1.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":[],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/machine-translation-on-wmt2014-english-german","task":"Machine Translation","dataset":"WMT2014 English-German","model":"Local Transformer","rank_in_archive_order":30,"of":91,"metrics":{"BLEU score":"29.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.10182","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}