{"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/neural-responding-machine-for-short-text","title":"Neural Responding Machine for Short-Text Conversation","arxiv_id":"1503.02364","date":"2015-03-09","proceeding":"IJCNLP 2015 7","authors":["Lifeng Shang","Zhengdong Lu","Hang Li"],"abstract":"We propose Neural Responding Machine (NRM), a neural network-based response\ngenerator for Short-Text Conversation. NRM takes the general encoder-decoder\nframework: it formalizes the generation of response as a decoding process based\non the latent representation of the input text, while both encoding and\ndecoding are realized with recurrent neural networks (RNN). The NRM is trained\nwith a large amount of one-round conversation data collected from a\nmicroblogging service. Empirical study shows that NRM can generate\ngrammatically correct and content-wise appropriate responses to over 75% of the\ninput text, outperforming state-of-the-arts in the same setting, including\nretrieval-based and SMT-based models.","url_abs":"http://arxiv.org/abs/1503.02364v2","url_pdf":"http://arxiv.org/pdf/1503.02364v2.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":"neural-responding-machine-for-short-text","repo_url":"https://github.com/EuphoriaYan/ChatRobot-For-Keras2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"neural-responding-machine-for-short-text","repo_url":"https://github.com/dongdong199408/teachchatrobot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"neural-responding-machine-for-short-text","repo_url":"https://github.com/thu-coai/CDial-GPT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"neural-responding-machine-for-short-text","repo_url":"https://github.com/thu-coai/ecm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"short-text-conversation","task_name":"Short-Text Conversation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1503.02364","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}