{"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/mojitalk-generating-emotional-responses-at","title":"MojiTalk: Generating Emotional Responses at Scale","arxiv_id":"1711.04090","date":"2017-11-11","proceeding":"ACL 2018 7","authors":["Xianda Zhou","William Yang Wang"],"abstract":"Generating emotional language is a key step towards building empathetic\nnatural language processing agents. However, a major challenge for this line of\nresearch is the lack of large-scale labeled training data, and previous studies\nare limited to only small sets of human annotated sentiment labels.\nAdditionally, explicitly controlling the emotion and sentiment of generated\ntext is also difficult. In this paper, we take a more radical approach: we\nexploit the idea of leveraging Twitter data that are naturally labeled with\nemojis. More specifically, we collect a large corpus of Twitter conversations\nthat include emojis in the response, and assume the emojis convey the\nunderlying emotions of the sentence. We then introduce a reinforced conditional\nvariational encoder approach to train a deep generative model on these\nconversations, which allows us to use emojis to control the emotion of the\ngenerated text. Experimentally, we show in our quantitative and qualitative\nanalyses that the proposed models can successfully generate high-quality\nabstractive conversation responses in accordance with designated emotions.","url_abs":"http://arxiv.org/abs/1711.04090v2","url_pdf":"http://arxiv.org/pdf/1711.04090v2.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":"mojitalk-generating-emotional-responses-at","repo_url":"https://github.com/ArponKundu/UIU_MojiTalk","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"mojitalk-generating-emotional-responses-at","repo_url":"https://github.com/Claude-Zhou/MojiTalk","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.04090","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.04090"}},"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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