{"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/towards-a-human-like-open-domain-chatbot","title":"Towards a Human-like Open-Domain Chatbot","arxiv_id":"2001.09977","date":"2020-01-27","proceeding":null,"authors":["Daniel Adiwardana","Minh-Thang Luong","David R. So","Jamie Hall","Noah Fiedel","Romal Thoppilan","Zi Yang","Apoorv Kulshreshtha","Gaurav Nemade","Yifeng Lu","Quoc V. Le"],"abstract":"We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token. We also propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of a human-like multi-turn conversation. Our experiments show strong correlation between perplexity and SSA. The fact that the best perplexity end-to-end trained Meena scores high on SSA (72% on multi-turn evaluation) suggests that a human-level SSA of 86% is potentially within reach if we can better optimize perplexity. Additionally, the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots we evaluated.","url_abs":"https://arxiv.org/abs/2001.09977v3","url_pdf":"https://arxiv.org/pdf/2001.09977v3.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":"towards-a-human-like-open-domain-chatbot","repo_url":"https://github.com/rustyoldrake/Character-Cartridges-Embodied-Identity","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"towards-a-human-like-open-domain-chatbot","repo_url":"https://github.com/nawnoes/pytorch-meena","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"chatbot","task_name":"Chatbot"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[{"method_slug":"meena","method_name":"Meena"}],"datasets_introduced":[],"methods_introduced":[{"slug":"meena","name":"Meena","full_name":"Meena"}],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2001.09977","atlas_url":"https://app.syntology.ai/?focus=2001.09977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.09977"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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