{"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/the-rllchatbot-a-solution-to-the-convai","title":"The RLLChatbot: a solution to the ConvAI challenge","arxiv_id":"1811.02714","date":"2018-11-07","proceeding":null,"authors":["Nicolas Gontier","Koustuv Sinha","Peter Henderson","Iulian Serban","Michael Noseworthy","Prasanna Parthasarathi","Joelle Pineau"],"abstract":"Current conversational systems can follow simple commands and answer basic\nquestions, but they have difficulty maintaining coherent and open-ended\nconversations about specific topics. Competitions like the Conversational\nIntelligence (ConvAI) challenge are being organized to push the research\ndevelopment towards that goal. This article presents in detail the RLLChatbot\nthat participated in the 2017 ConvAI challenge. The goal of this research is to\nbetter understand how current deep learning and reinforcement learning tools\ncan be used to build a robust yet flexible open domain conversational agent. We\nprovide a thorough description of how a dialog system can be built and trained\nfrom mostly public-domain datasets using an ensemble model. The first\ncontribution of this work is a detailed description and analysis of different\ntext generation models in addition to novel message ranking and selection\nmethods. Moreover, a new open-source conversational dataset is presented.\nTraining on this data significantly improves the Recall@k score of the ranking\nand selection mechanisms compared to our baseline model responsible for\nselecting the message returned at each interaction.","url_abs":"http://arxiv.org/abs/1811.02714v2","url_pdf":"http://arxiv.org/pdf/1811.02714v2.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":"the-rllchatbot-a-solution-to-the-convai","repo_url":"https://github.com/NicolasAG/convai","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}