{"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/response-generation-by-context-aware","title":"Response Generation by Context-aware Prototype Editing","arxiv_id":"1806.07042","date":"2018-06-19","proceeding":null,"authors":["Yu Wu","Furu Wei","Shaohan Huang","Yunli Wang","Zhoujun Li","Ming Zhou"],"abstract":"Open domain response generation has achieved remarkable progress in recent\nyears, but sometimes yields short and uninformative responses. We propose a new\nparadigm for response generation, that is response generation by editing, which\nsignificantly increases the diversity and informativeness of the generation\nresults. Our assumption is that a plausible response can be generated by\nslightly revising an existing response prototype. The prototype is retrieved\nfrom a pre-defined index and provides a good start-point for generation because\nit is grammatical and informative. We design a response editing model, where an\nedit vector is formed by considering differences between a prototype context\nand a current context, and then the edit vector is fed to a decoder to revise\nthe prototype response for the current context. Experiment results on a large\nscale dataset demonstrate that the response editing model outperforms\ngenerative and retrieval-based models on various aspects.","url_abs":"http://arxiv.org/abs/1806.07042v4","url_pdf":"http://arxiv.org/pdf/1806.07042v4.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":"response-generation-by-context-aware","repo_url":"https://github.com/MarkWuNLP/ResponseEdit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"response-generation-by-context-aware","repo_url":"https://github.com/jimth001/Bi-Seq2Seq","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"response-generation-by-context-aware","repo_url":"https://github.com/xingyuliuNLP/ResponseEdit_FR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"informativeness","task_name":"Informativeness"},{"task_slug":"response-generation","task_name":"Response Generation"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.07042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.07042"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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