{"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/a-multimodal-lstm-for-predicting-listener","title":"A Multimodal LSTM for Predicting Listener Empathic Responses Over Time","arxiv_id":"1812.04891","date":"2018-12-12","proceeding":null,"authors":["Zhi-Xuan Tan","Arushi Goel","Thanh-Son Nguyen","Desmond C. Ong"],"abstract":"People naturally understand the emotions of-and often also empathize\nwith-those around them. In this paper, we predict the emotional valence of an\nempathic listener over time as they listen to a speaker narrating a life story.\nWe use the dataset provided by the OMG-Empathy Prediction Challenge, a workshop\nheld in conjunction with IEEE FG 2019. We present a multimodal LSTM model with\nfeature-level fusion and local attention that predicts empathic responses from\naudio, text, and visual features. Our best-performing model, which used only\nthe audio and text features, achieved a concordance correlation coefficient\n(CCC) of 0.29 and 0.32 on the Validation set for the Generalized and\nPersonalized track respectively, and achieved a CCC of 0.14 and 0.14 on the\nheld-out Test set. We discuss the difficulties faced and the lessons learnt\ntackling this challenge.","url_abs":"http://arxiv.org/abs/1812.04891v2","url_pdf":"http://arxiv.org/pdf/1812.04891v2.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":"a-multimodal-lstm-for-predicting-listener","repo_url":"https://github.com/desmond-ong/cheem-omg-empathy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.04891","atlas_url":"https://app.syntology.ai/?focus=1812.04891","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}