ArticleProceedings of the conference. Association for Computational Linguistics. Meeting2018
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment.
Article in Proceedings of the conference. Association for Computational Linguistics. Meeting, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- A text guided multimodal scale path fusion network for multimodal sentiment analysis.Scientific reports · 2025Article
- DAST-GAN: An Adversarial Learning Multimodal Sentiment Analysis Model Based on Dynamic Attention and Spatio-Temporal Fusion.Sensors (Basel, Switzerland) · 2025Article
- Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Deep learning on multi-view sequential data: a survey.Artificial intelligence review · 2023Article
- LGCCT: A Light Gated and Crossed Complementation Transformer for Multimodal Speech Emotion Recognition.Entropy (Basel, Switzerland) · 2022Article
- Region Dual Attention-Based Video Emotion Recognition.Computational intelligence and neuroscience · 2022Article
- Research on Integration of Emotion Analysis in English Modular Teaching Based on Natural Language Processing.Frontiers in psychology · 2022Article
- Affective Latent Representation of Acoustic and Lexical Features for Emotion Recognition.Sensors (Basel, Switzerland) · 2020Article
- Mutual Correlation Attentive Factors in Dyadic Fusion Networks for Speech Emotion Recognition.Proceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia · 2019Article
- Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2019Article
- Multimodal Transformer for Unaligned Multimodal Language Sequences.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2019Article
- Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network.Sensors (Basel, Switzerland) · 2019Article
- Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
- Human Conversation Analysis Using Attentive Multimodal Networks with Hierarchical Encoder-Decoder.Proceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Multimodal affective computing, learning to recognize and interpret human affect and subjective information from multiple data sources, is still challenging because:(i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract levels, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data. Our introduced model outperforms state-of-the-art approaches on published datasets, and we demonstrate that our model's synchronized attention over modalities offers visual interpretability.
Identifiers
30505068PMC6261375What 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.