ArticleProceedings of the conference. Association for Computational Linguistics. Meeting2019
Multimodal Transformer for Unaligned Multimodal Language Sequences.
Article in Proceedings of the conference. Association for Computational Linguistics. Meeting, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
138 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Development and testing of a public health emergency intelligence analysis system based on text analysis and NLP analysis.Frontiers in public health · 2025Pooled it
- Machine learning approaches to anxiety detection: trends, model evaluation, and future directions.Frontiers in artificial intelligence · 2025Pooled it
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- A Consistency-Aware Multimodal Sensing Framework for Short-Term Cross-Border Prediction Under Dynamic Trade.Sensors (Basel, Switzerland) · 2026Article
- A Quantum-Inspired Residual Self-Attention Network for Multimodal Sentiment Analysis.Entropy (Basel, Switzerland) · 2026Article
- Smart Furniture-Embedded Bed-Interaction and Multimodal Room Sensing with Sleep-Aware Hierarchical Fusion for Control-Oriented Bedroom State Recognition.Sensors (Basel, Switzerland) · 2026Article
- A Transferable Genomic Language Model Framework for Fungal Gene Essentiality Prediction.bioRxiv : the preprint server for biology · 2026Article
- Structured Prototype Learning with Feature Fusion for Sparse and Asynchronous Audio-Visual Depression Recognition.Sensors (Basel, Switzerland) · 2026Article
- Fusion of Radiomics and Gated Graph Attention Network for Pulmonary Nodule Malignancy Classification.Journal of imaging · 2026Article
- Pretrained Financial Language Model-Guided Multimodal Sensing with Hardware Provenance and Cross-Frequency Temporal Alignment for Event Prediction.Sensors (Basel, Switzerland) · 2026Article
- Reliability-Aware Cross-Modal Learning Behavior Sensing for Student Cognitive Bias Recognition and Teaching-Oriented Psychological Risk Warning.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Driven Sensing of Cross-Border Trade Risks Through Declaration-to-Physical-Fact Alignment and Evidence-Grounded Question Answering.Sensors (Basel, Switzerland) · 2026Article
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- Modality Effectiveness in Cross-Subject Sign Language Recognition: A Skeleton Perspective.Sensors (Basel, Switzerland) · 2026Article
- QGFace-LLaVA: Quality-Aware Controlled Fusion of Structured Side Information for Face Analysis Under Imperfect Metadata.Biomimetics (Basel, Switzerland) · 2026Article
- Sensor-Based Cross-Modal Spatiotemporal Alignment and Causal Profit Modeling for Agricultural Input Optimization.Sensors (Basel, Switzerland) · 2026Article
- Asynchronous Cross-Modal Dynamic Graph Learning for Intelligent Sensing of AI Computing Infrastructure Expansion.Sensors (Basel, Switzerland) · 2026Article
- Multisource Sensor Fusion and Large Language Model Integration for Explainable State Perception and Anomaly Awareness.Sensors (Basel, Switzerland) · 2026Article
78 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. However, two major challenges in modeling such multimodal human language time-series data exist: 1) inherent data non-alignment due to variable sampling rates for the sequences from each modality; and 2) long-range dependencies between elements across modalities. In this paper, we introduce the Multimodal Transformer (MulT) to generically address the above issues in an end-to-end manner without explicitly aligning the data. At the heart of our model is the directional pairwise cross-modal attention, which attends to interactions between multimodal sequences across distinct time steps and latently adapt streams from one modality to another. Comprehensive experiments on both aligned and non-aligned multimodal time-series show that our model outperforms state-of-the-art methods by a large margin. In addition, empirical analysis suggests that correlated crossmodal signals are able to be captured by the proposed crossmodal attention mechanism in MulT.
Identifiers
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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.