{"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/surprisingly-easy-hard-attention-for-sequence","title":"Surprisingly Easy Hard-Attention for Sequence to Sequence Learning","arxiv_id":null,"date":"2018-10-01","proceeding":"EMNLP 2018 10","authors":["Shiv Shankar","Siddhant Garg","Sunita Sarawagi"],"abstract":"In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On five translation tasks we show effortless and consistent gains in BLEU compared to existing attention mechanisms.","url_abs":"https://aclanthology.org/D18-1065","url_pdf":"https://aclanthology.org/D18-1065.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":"surprisingly-easy-hard-attention-for-sequence","repo_url":"https://github.com/sid7954/beam-joint-attention","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"hard-attention","task_name":"Hard Attention"},{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"morphological-inflection","task_name":"Morphological Inflection"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}