{"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-neural-network-approach-to-context-1","title":"A Neural Network Approach to Context-Sensitive Generation of Conversational Responses","arxiv_id":"1506.06714","date":"2015-06-22","proceeding":"HLT 2015 5","authors":["Alessandro Sordoni","Michel Galley","Michael Auli","Chris Brockett","Yangfeng Ji","Margaret Mitchell","Jian-Yun Nie","Jianfeng Gao","Bill Dolan"],"abstract":"We present a novel response generation system that can be trained end to end\non large quantities of unstructured Twitter conversations. A neural network\narchitecture is used to address sparsity issues that arise when integrating\ncontextual information into classic statistical models, allowing the system to\ntake into account previous dialog utterances. Our dynamic-context generative\nmodels show consistent gains over both context-sensitive and\nnon-context-sensitive Machine Translation and Information Retrieval baselines.","url_abs":"http://arxiv.org/abs/1506.06714v1","url_pdf":"http://arxiv.org/pdf/1506.06714v1.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":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"response-generation","task_name":"Response Generation"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[{"slug":"microsoft-research-social-media-conversation","name":"Microsoft Research Social Media Conversation Corpus","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1506.06714","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}