{"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/multimodal-language-analysis-with-recurrent","title":"Multimodal Language Analysis with Recurrent Multistage Fusion","arxiv_id":"1808.03920","date":"2018-08-12","proceeding":"EMNLP 2018 10","authors":["Paul Pu Liang","Ziyin Liu","Amir Zadeh","Louis-Philippe Morency"],"abstract":"Computational modeling of human multimodal language is an emerging research\narea in natural language processing spanning the language, visual and acoustic\nmodalities. Comprehending multimodal language requires modeling not only the\ninteractions within each modality (intra-modal interactions) but more\nimportantly the interactions between modalities (cross-modal interactions). In\nthis paper, we propose the Recurrent Multistage Fusion Network (RMFN) which\ndecomposes the fusion problem into multiple stages, each of them focused on a\nsubset of multimodal signals for specialized, effective fusion. Cross-modal\ninteractions are modeled using this multistage fusion approach which builds\nupon intermediate representations of previous stages. Temporal and intra-modal\ninteractions are modeled by integrating our proposed fusion approach with a\nsystem of recurrent neural networks. The RMFN displays state-of-the-art\nperformance in modeling human multimodal language across three public datasets\nrelating to multimodal sentiment analysis, emotion recognition, and speaker\ntraits recognition. We provide visualizations to show that each stage of fusion\nfocuses on a different subset of multimodal signals, learning increasingly\ndiscriminative multimodal representations.","url_abs":"http://arxiv.org/abs/1808.03920v1","url_pdf":"http://arxiv.org/pdf/1808.03920v1.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":"multimodal-language-analysis-with-recurrent","repo_url":"https://github.com/righ120/multimodal_nlp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"multimodal-sentiment-analysis","task_name":"Multimodal Sentiment Analysis"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.03920","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}