{"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/learning-symmetric-collaborative-dialogue","title":"Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings","arxiv_id":"1704.07130","date":"2017-04-24","proceeding":"ACL 2017 7","authors":["He He","Anusha Balakrishnan","Mihail Eric","Percy Liang"],"abstract":"We study a symmetric collaborative dialogue setting in which two agents, each\nwith private knowledge, must strategically communicate to achieve a common\ngoal. The open-ended dialogue state in this setting poses new challenges for\nexisting dialogue systems. We collected a dataset of 11K human-human dialogues,\nwhich exhibits interesting lexical, semantic, and strategic elements. To model\nboth structured knowledge and unstructured language, we propose a neural model\nwith dynamic knowledge graph embeddings that evolve as the dialogue progresses.\nAutomatic and human evaluations show that our model is both more effective at\nachieving the goal and more human-like than baseline neural and rule-based\nmodels.","url_abs":"http://arxiv.org/abs/1704.07130v1","url_pdf":"http://arxiv.org/pdf/1704.07130v1.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":"learning-symmetric-collaborative-dialogue","repo_url":"https://worksheets.codalab.org/worksheets/0xc757f29f5c794e5eb7bfa8ca9c945573","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"learning-symmetric-collaborative-dialogue","repo_url":"https://github.com/stanfordnlp/cocoa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"knowledge-graph-embeddings","task_name":"Knowledge Graph Embeddings"}],"methods":[],"datasets_introduced":[{"slug":"mutualfriends","name":"MutualFriends","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.07130","atlas_url":"https://app.syntology.ai/?focus=1704.07130","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}