{"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/training-end-to-end-dialogue-systems-with-the","title":"Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus","arxiv_id":null,"date":"2017-01-01","proceeding":null,"authors":["Ryan Lowe","Nissan Pow","Iulian Vlad Serban","Laurent Charlin","Chia-Wei Liu","Joelle Pineau"],"abstract":"In this paper, we analyze neural network-based dialogue systems trained in an end-to-end manner using an updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because of its size, long context lengths, and technical nature; thus, it can be used to train large models directly from data with minimal feature engineering. We provide baselines in two different environments: one where models are trained to select the correct next response from a list of candidate responses, and one where models are trained to maximize the loglikelihood of a generated utterance conditioned on the context of the conversation. These are both evaluated on a recall task that we call next utterance classification (NUC), and using vector-based metrics that capture the topicality of the responses. We observe that current end-to-end models are unable to completely solve these tasks; thus, we provide a qualitative error analysis to determine the primary causes of error for end-to-end models evaluated on NUC, and examine sample utterances from the generative models. As a result of this analysis, we suggest some promising directions for future research on the Ubuntu Dialogue Corpus, which can also be applied to end-to-end dialogue systems in general.","url_abs":"https://core.ac.uk/download/pdf/230775856.pdf","url_pdf":"https://core.ac.uk/download/pdf/230775856.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":[],"tasks":[{"task_slug":"conversation-disentanglement","task_name":"Conversation Disentanglement"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/conversation-disentanglement-on-linux-irc-ch2-1","task":"Conversation Disentanglement","dataset":"Linux IRC (Ch2 Elsner)","model":"Heuristic","rank_in_archive_order":3,"of":3,"metrics":{"1-1":"45.1","Local":"73.8","Shen F-1":"51.8"},"uses_additional_data":false},{"leaderboard":"/sota/conversation-disentanglement-on-linux-irc-ch2","task":"Conversation Disentanglement","dataset":"Linux IRC (Ch2 Kummerfeld)","model":"Heuristic","rank_in_archive_order":3,"of":3,"metrics":{"1-1":"43.4","Local":"67.9","Shen F-1":"50.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}