{"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/building-a-conversational-agent-overnight","title":"Building a Conversational Agent Overnight with Dialogue Self-Play","arxiv_id":"1801.04871","date":"2018-01-15","proceeding":null,"authors":["Pararth Shah","Dilek Hakkani-Tür","Gokhan Tür","Abhinav Rastogi","Ankur Bapna","Neha Nayak","Larry Heck"],"abstract":"We propose Machines Talking To Machines (M2M), a framework combining\nautomation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents\nfor goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with\njust a task schema and an API client from the dialogue system developer, but it\nis also customizable to cater to task-specific interactions. Compared to the\nWizard-of-Oz approach for data collection, M2M achieves greater diversity and\ncoverage of salient dialogue flows while maintaining the naturalness of\nindividual utterances. In the first phase, a simulated user bot and a\ndomain-agnostic system bot converse to exhaustively generate dialogue\n\"outlines\", i.e. sequences of template utterances and their semantic parses. In\nthe second phase, crowd workers provide contextual rewrites of the dialogues to\nmake the utterances more natural while preserving their meaning. The entire\nprocess can finish within a few hours. We propose a new corpus of 3,000\ndialogues spanning 2 domains collected with M2M, and present comparisons with\npopular dialogue datasets on the quality and diversity of the surface forms and\ndialogue flows.","url_abs":"http://arxiv.org/abs/1801.04871v1","url_pdf":"http://arxiv.org/pdf/1801.04871v1.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":"building-a-conversational-agent-overnight","repo_url":"https://github.com/google-research-datasets/simulated-dialogue","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"building-a-conversational-agent-overnight","repo_url":"https://github.com/marcomanciniunitn/Master-Thesis-Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"building-a-conversational-agent-overnight","repo_url":"https://github.com/tatianapassali/artificial-disfluency-generation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.04871","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}