{"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/evaluating-prerequisite-qualities-for","title":"Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems","arxiv_id":"1511.06931","date":"2015-11-21","proceeding":null,"authors":["Jesse Dodge","Andreea Gane","Xiang Zhang","Antoine Bordes","Sumit Chopra","Alexander Miller","Arthur Szlam","Jason Weston"],"abstract":"A long-term goal of machine learning is to build intelligent conversational\nagents. One recent popular approach is to train end-to-end models on a large\namount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals\n& Le, 2015; Shang et al., 2015). However, this approach leaves many questions\nunanswered as an understanding of the precise successes and shortcomings of\neach model is hard to assess. A contrasting recent proposal are the bAbI tasks\n(Weston et al., 2015b) which are synthetic data that measure the ability of\nlearning machines at various reasoning tasks over toy language. Unfortunately,\nthose tests are very small and hence may encourage methods that do not scale.\nIn this work, we propose a suite of new tasks of a much larger scale that\nattempt to bridge the gap between the two regimes. Choosing the domain of\nmovies, we provide tasks that test the ability of models to answer factual\nquestions (utilizing OMDB), provide personalization (utilizing MovieLens),\ncarry short conversations about the two, and finally to perform on natural\ndialogs from Reddit. We provide a dataset covering 75k movie entities and with\n3.5M training examples. We present results of various models on these tasks,\nand evaluate their performance.","url_abs":"http://arxiv.org/abs/1511.06931v6","url_pdf":"http://arxiv.org/pdf/1511.06931v6.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":"evaluating-prerequisite-qualities-for","repo_url":"https://github.com/michaelfarrell76/End-To-End-Generative-Dialogue","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"mdd","name":"MDD","full_name":"Movie Dialog dataset"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1511.06931","atlas_url":"https://app.syntology.ai/?focus=1511.06931","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}