{"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-end-to-end-goal-oriented-dialog","title":"Learning End-to-End Goal-Oriented Dialog","arxiv_id":"1605.07683","date":"2016-05-24","proceeding":null,"authors":["Antoine Bordes","Y-Lan Boureau","Jason Weston"],"abstract":"Traditional dialog systems used in goal-oriented applications require a lot\nof domain-specific handcrafting, which hinders scaling up to new domains.\nEnd-to-end dialog systems, in which all components are trained from the dialogs\nthemselves, escape this limitation. But the encouraging success recently\nobtained in chit-chat dialog may not carry over to goal-oriented settings. This\npaper proposes a testbed to break down the strengths and shortcomings of\nend-to-end dialog systems in goal-oriented applications. Set in the context of\nrestaurant reservation, our tasks require manipulating sentences and symbols,\nso as to properly conduct conversations, issue API calls and use the outputs of\nsuch calls. We show that an end-to-end dialog system based on Memory Networks\ncan reach promising, yet imperfect, performance and learn to perform\nnon-trivial operations. We confirm those results by comparing our system to a\nhand-crafted slot-filling baseline on data from the second Dialog State\nTracking Challenge (Henderson et al., 2014a). We show similar result patterns\non data extracted from an online concierge service.","url_abs":"http://arxiv.org/abs/1605.07683v4","url_pdf":"http://arxiv.org/pdf/1605.07683v4.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-end-to-end-goal-oriented-dialog","repo_url":"https://github.com/candoz/memn2n-dialog-babi-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"learning-end-to-end-goal-oriented-dialog","repo_url":"https://github.com/chaitjo/personalized-dialog","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-end-to-end-goal-oriented-dialog","repo_url":"https://github.com/facebookresearch/ParlAI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-end-to-end-goal-oriented-dialog","repo_url":"https://github.com/joe-prog/https-github.com-facebookresearch-ParlAI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"gone","observed_at":"2026-09-18","how":"tree_404+repo_404"}},{"paper_slug":"learning-end-to-end-goal-oriented-dialog","repo_url":"https://github.com/voicy-ai/DialogStateTracking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"learning-end-to-end-goal-oriented-dialog","repo_url":"https://github.com/ysglh/Task-Oriented-Dialogue-Dataset-Survey","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"goal-oriented-dialog","task_name":"Goal-Oriented Dialog"},{"task_slug":"slot-filling","task_name":"Slot Filling"},{"task_slug":null,"task_name":"dialog state tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.07683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.07683"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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