{"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/higru-hierarchical-gated-recurrent-units-for","title":"HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition","arxiv_id":"1904.04446","date":"2019-04-09","proceeding":"NAACL 2019 6","authors":["Wenxiang Jiao","Haiqin Yang","Irwin King","Michael R. Lyu"],"abstract":"In this paper, we address three challenges in utterance-level emotion\nrecognition in dialogue systems: (1) the same word can deliver different\nemotions in different contexts; (2) some emotions are rarely seen in general\ndialogues; (3) long-range contextual information is hard to be effectively\ncaptured. We therefore propose a hierarchical Gated Recurrent Unit (HiGRU)\nframework with a lower-level GRU to model the word-level inputs and an\nupper-level GRU to capture the contexts of utterance-level embeddings.\nMoreover, we promote the framework to two variants, HiGRU with individual\nfeatures fusion (HiGRU-f) and HiGRU with self-attention and features fusion\n(HiGRU-sf), so that the word/utterance-level individual inputs and the\nlong-range contextual information can be sufficiently utilized. Experiments on\nthree dialogue emotion datasets, IEMOCAP, Friends, and EmotionPush demonstrate\nthat our proposed HiGRU models attain at least 8.7%, 7.5%, 6.0% improvement\nover the state-of-the-art methods on each dataset, respectively. Particularly,\nby utilizing only the textual feature in IEMOCAP, our HiGRU models gain at\nleast 3.8% improvement over the state-of-the-art conversational memory network\n(CMN) with the trimodal features of text, video, and audio.","url_abs":"http://arxiv.org/abs/1904.04446v1","url_pdf":"http://arxiv.org/pdf/1904.04446v1.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":"higru-hierarchical-gated-recurrent-units-for","repo_url":"https://github.com/wxjiao/HiGRUs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"}],"methods":[{"method_slug":"gru","method_name":"GRU"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.04446","atlas_url":"https://app.syntology.ai/?focus=1904.04446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04446"}},"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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