{"title":"$\\mathcal {P}$owMix: A Versatile Regularizer for Multimodal Sentiment Analysis","authors":"Efthymios Georgiou;Yannis Avrithis;Alexandros Potamianos","doi":"10.1109/TASLP.2024.3496316","DOIUrl":null,"url":null,"abstract":"Multimodal sentiment analysis (MSA) leverages heterogeneous data sources to interpret the complex nature of human sentiments. Despite significant progress in multimodal architecture design, the field lacks comprehensive regularization methods. This paper introduces \n<inline-formula><tex-math>$\\mathcal {P}$</tex-math></inline-formula>\nowMix, a versatile embedding space regularizer that builds upon the strengths of unimodal mixing-based regularization approaches and introduces novel algorithmic components that are specifically tailored to multimodal tasks. \n<inline-formula><tex-math>$\\mathcal {P}$</tex-math></inline-formula>\nowMix is integrated before the fusion stage of multimodal architectures and facilitates intra-modal mixing, such as mixing text with text, to act as a regularizer. \n<inline-formula><tex-math>$\\mathcal {P}$</tex-math></inline-formula>\nowMix consists of five components: 1) a varying number of generated mixed examples, 2) mixing factor reweighting, 3) anisotropic mixing, 4) dynamic mixing, and 5) cross-modal label mixing. Extensive experimentation across benchmark MSA datasets and a broad spectrum of diverse architectural designs demonstrate the efficacy of \n<inline-formula><tex-math>$\\mathcal {P}$</tex-math></inline-formula>\nowMix, as evidenced by consistent performance improvements over baselines and existing mixing methods. An in-depth ablation study highlights the critical contribution of each \n<inline-formula><tex-math>$\\mathcal {P}$</tex-math></inline-formula>\nowMix component and how they synergistically enhance performance. Furthermore, algorithmic analysis demonstrates how \n<inline-formula><tex-math>$\\mathcal {P}$</tex-math></inline-formula>\nowMix behaves in different scenarios, particularly comparing early versus late fusion architectures. Notably, \n<inline-formula><tex-math>$\\mathcal {P}$</tex-math></inline-formula>\nowMix enhances overall performance without sacrificing model robustness or magnifying text dominance. It also retains its strong performance in situations of limited data. Our findings position \n<inline-formula><tex-math>$\\mathcal {P}$</tex-math></inline-formula>\nowMix as a promising versatile regularization strategy for MSA.","PeriodicalId":13332,"journal":{"name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","volume":"32 ","pages":"5010-5023"},"PeriodicalIF":4.1000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10750299/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Multimodal sentiment analysis (MSA) leverages heterogeneous data sources to interpret the complex nature of human sentiments. Despite significant progress in multimodal architecture design, the field lacks comprehensive regularization methods. This paper introduces
$\mathcal {P}$
owMix, a versatile embedding space regularizer that builds upon the strengths of unimodal mixing-based regularization approaches and introduces novel algorithmic components that are specifically tailored to multimodal tasks.
$\mathcal {P}$
owMix is integrated before the fusion stage of multimodal architectures and facilitates intra-modal mixing, such as mixing text with text, to act as a regularizer.
$\mathcal {P}$
owMix consists of five components: 1) a varying number of generated mixed examples, 2) mixing factor reweighting, 3) anisotropic mixing, 4) dynamic mixing, and 5) cross-modal label mixing. Extensive experimentation across benchmark MSA datasets and a broad spectrum of diverse architectural designs demonstrate the efficacy of
$\mathcal {P}$
owMix, as evidenced by consistent performance improvements over baselines and existing mixing methods. An in-depth ablation study highlights the critical contribution of each
$\mathcal {P}$
owMix component and how they synergistically enhance performance. Furthermore, algorithmic analysis demonstrates how
$\mathcal {P}$
owMix behaves in different scenarios, particularly comparing early versus late fusion architectures. Notably,
$\mathcal {P}$
owMix enhances overall performance without sacrificing model robustness or magnifying text dominance. It also retains its strong performance in situations of limited data. Our findings position
$\mathcal {P}$
owMix as a promising versatile regularization strategy for MSA.
期刊介绍:
The IEEE/ACM Transactions on Audio, Speech, and Language Processing covers audio, speech and language processing and the sciences that support them. In audio processing: transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. In speech processing: areas such as speech analysis, synthesis, coding, speech and speaker recognition, speech production and perception, and speech enhancement. In language processing: speech and text analysis, understanding, generation, dialog management, translation, summarization, question answering and document indexing and retrieval, as well as general language modeling.