Integrating Multimodal Information in Large Pretrained Transformers (2020.acl-main)
Copied to clipboard
Wasifur Rahman, Md Kamrul Hasan, Sangwu Lee, AmirAli Bagher Zadeh, Chengfeng Mao, Louis-Philippe Morency, Ehsan Hoque
| Challenge: | Recent Transformer-based contextual word representations have shown state-of-the-art performance in multiple disciplines within NLP. |
| Approach: | They propose an attachment to BERT and XLNet that allows them to accept multimodal nonverbal data during fine-tuning. |
| Outcome: | The proposed attachment allows BERT and XLNet to accept multimodal nonverbal data during fine-tuning. |
Similar Papers
MA-BERT: Learning Representation by Incorporating Multi-Attribute Knowledge in Transformers (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for incorporating external attribute knowledge into deep neural networks are concatenating multiple attributes to word/text representation or treating them as biases to adjust attention distribution. |
| Approach: | They propose a multi-attribute BERT to incorporate external attribute knowledge into deep neural networks. |
| Outcome: | The proposed method outperforms existing models and models on three benchmark datasets. |
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph (P18-1)
Copied to clipboard
| Challenge: | Analyzing human multimodal language is emerging area of research in NLP. |
| Approach: | They propose a multimodal fusion technique to exploit how modalities interact in multimodal language. |
| Outcome: | The proposed technique exploits how modalities interact with each other in human multimodal language. |
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)
Copied to clipboard
| Challenge: | Multimodal Sentiment Analysis (MSA) is effective when using rich information from multiple sources, but the potential sentiment-irrelevant information across modalities may hinder the performance from being further improved. |
| Approach: | They propose an Adaptive Language-guided Multimodal Transformer (ALMT) that learns an irrelevance/conflict-suppressing representation from visual and audio features under guidance of language features at different scales. |
| Outcome: | The proposed model achieves state-of-the-art on several popular datasets and an abundance of ablation shows the effectiveness of the proposed model. |
Dynamic Regularization in UDA for Transformers in Multimodal Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Multimodal machine learning is a cutting-edge field that explores ways to combine information from multiple sources into models. |
| Approach: | They propose a multimodal BERT-ViT model that exploits weaker modality while regularizing the loss function. |
| Outcome: | The proposed model exploits weaker modality while regularizing the loss function. |
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies? (2022.emnlp-main)
Copied to clipboard
| Challenge: | a recent study examines whether vision-and-language models learn syntactic dependencies . a controlled evaluation of the models is crucial for a precise and rigorous test of their knowledge . |
| Approach: | They propose a task to evaluate understanding of predicate-noun dependencies in a controlled setup. |
| Outcome: | This study compares state-of-the-art models with a case study on predicate-noun dependencies. |
Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs (2021.tacl-1)
Copied to clipboard
| Challenge: | Large-scale pretraining and task-specific fine-tuning are now the standard methodology for many tasks in computer vision and natural language processing. |
| Approach: | They propose to combine two types of vision and language BERTs to create a theoretical framework that can be unified under different theoretical frameworks. |
| Outcome: | The proposed models can be classified into single-stream or dual-stream encoders and are unified under a single theoretical framework. |
Word Representation Learning in Multimodal Pre-Trained Transformers: An Intrinsic Evaluation (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing models for linguistic representations of words are based on information extracted from large text corpora, and the sensory-motor experiences humans have with the world play an important role in determining word meaning. |
| Approach: | They propose to use contextualized word representations to learn semantic representations of words that align with human semantic intuitions. |
| Outcome: | The proposed models are shown to be more efficient on concrete word pairs than on abstract ones. |
Improving Multimodal fusion via Mutual Dependency Maximisation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Multimodal sentiment analysis is a trending area of research, and multimodal fusion is one of its most active topics. |
| Approach: | They propose to use modality-based penalties to measure dependency between models to improve accuracy. |
| Outcome: | The proposed methods improve accuracy on two well-known sentiment analysis datasets by 4.3 on the proposed models and by-product includes a statistical network which can interpret the high dimensional representations learnt by the model. |
Self-supervised Cross-modal Pretraining for Speech Emotion Recognition and Sentiment Analysis (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to multimodal speech emotion recognition and sentiment analysis have not improved results due to their relatively simple fusion mechanisms and lack of proper cross-modal pretraining. |
| Approach: | They propose a deep-fused audio-text bi-modal transformer with carefully designed cross-modal fusion mechanism and stage-wise cross-mod pretraining scheme to facilitate cross-modulation. |
| Outcome: | The proposed method exceeds benchmarks on public IEMOCAP emotion and CMU-MOSEI sentiment datasets by a large margin. |
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for audio-visual speech recognition use extra data to increase performance . a recent study shows that the use of unimodal self-supervised learning improves performance on multimodal tasks. |
| Approach: | They propose to use unimodal self-supervised learning to train AVSR models on unlabelled unilateral data. |
| Outcome: | The proposed model improves on lip reading sentences 2 by 30% even without an external language model. |