ArticleProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence2019
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors.
Article in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Multimodal prototypical network for interpretable sentiment classification.Scientific reports · 2025Article
- Is it in their words? Teachers' enthusiasm and their natural language in class-A sentiment analysis approach.The British journal of educational psychology · 2025Article
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- Analysis of the fusion of multimodal sentiment perception and physiological signals in Chinese-English cross-cultural communication: Transformer approach incorporating self-attention enhancement.PeerJ. Computer science · 2025Article
- Research on Multimodal Fusion of Temporal Electronic Medical Records.Bioengineering (Basel, Switzerland) · 2024Article
- A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face.Entropy (Basel, Switzerland) · 2023Review
- Application of big data and artificial intelligence in epidemic surveillance and containment.Intelligent medicine · 2023Review
- Deep learning on multi-view sequential data: a survey.Artificial intelligence review · 2023Article
- Multimodal Emotion Recognition Based on Cascaded Multichannel and Hierarchical Fusion.Computational intelligence and neuroscience · 2023Article
- GCFFrontiers in neuroscience · 2023Article
- Sentiment Analysis and Emotion Recognition from Speech Using Universal Speech Representations.Sensors (Basel, Switzerland) · 2022Article
- LGCCT: A Light Gated and Crossed Complementation Transformer for Multimodal Speech Emotion Recognition.Entropy (Basel, Switzerland) · 2022Article
- Dynamic Invariant-Specific Representation Fusion Network for Multimodal Sentiment Analysis.Computational intelligence and neuroscience · 2022Article
- AFR-BERT: Attention-based mechanism feature relevance fusion multimodal sentiment analysis model.PloS one · 2022Article
- Multimodal Sentiment Analysis Based on Cross-Modal Attention and Gated Cyclic Hierarchical Fusion Networks.Computational intelligence and neuroscience · 2022Article
- Multimodal Deep Learning Models for Detecting Dementia From Speech and Transcripts.Frontiers in aging neuroscience · 2022Article
- Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2020Article
- Integrating Multimodal Information in Large Pretrained Transformers.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear. To better model human language, we first model expressive nonverbal representations by analyzing the fine-grained visual and acoustic patterns that occur during word segments. In addition, we seek to capture the dynamic nature of nonverbal intents by shifting word representations based on the accompanying nonverbal behaviors. To this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN) that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues. Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition. We also visualize the shifted word representations in different nonverbal contexts and summarize common patterns regarding multimodal variations of word representations.
Identifiers
32219010PMC7098710What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.