| Challenge: | Existing models that represent word meanings from word co-occurrences ignore associations between modalities and lack ability to transfer information between . |
| Approach: | They propose a novel associative multichannel autoencoder that integrates textual, visual and auditory inputs to learn multimodal word representations. |
| Outcome: | The proposed model outperforms strong unimodal models and state-of-the-art models on six benchmark concepts similarity tests. |
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Retrieving Multimodal Information for Augmented Generation: A Survey (2023.findings-emnlp)
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Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
| Challenge: | Large Language Models (LLMs) are increasingly using multimodality to augment their generation ability, but there is no unified perception of at which stage and how to incorporate different modalities. |
| Approach: | They propose to use multimodality to augment Large Language Models (LLMs) this will provide scholars with a deeper understanding of the methods' applications and encourage them to adapt existing techniques to the fast-growing field of LLMs. |
| Outcome: | The proposed methods improve factuality, reasoning, interpretability, and robustness of the generated content. |
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks (2026.findings-acl)
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| Challenge: | Survey aims to identify challenges of multimodal unlearning for vision, language, audio and video . retraining after deletion requests or policy updates is often impractical, survey finds . |
| Approach: | They propose to enable selective removal across modalities while retaining overall utility. |
| Outcome: | This study compares models with existing models to identify weaknesses and improves performance. |
Word Representation Learning in Multimodal Pre-Trained Transformers: An Intrinsic Evaluation (2021.tacl-1)
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| 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. |
How do Multimodal Foundation Models Encode Text and Speech? An Analysis of Cross-Lingual and Cross-Modal Representations (2025.naacl-short)
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| Challenge: | Recent advances in foundation models have sparked growing interest in expanding their text processing capabilities to speech. |
| Approach: | They analyze the model activations from semantically equivalent sentences across languages in the text and speech modalities and examine how text and spoken are represented in recent multimodal foundation models. |
| Outcome: | The proposed models exhibit cross-lingual differences, but are not explicitly trained for modality-agnostic representations. |
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition (2022.acl-long)
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| 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. |
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)
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| 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. |
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis (2022.findings-emnlp)
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| Challenge: | Recent work on multimodal representation learning has focused on uni-modality pre-training or cross-modalities integration. |
| Approach: | They propose a framework for multimodal representation learning that uses uni-modal contrastive coding and an efficient unimodal feature augmentation strategy to capture intermodal dynamics. |
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MCSE: Multimodal Contrastive Learning of Sentence Embeddings (2022.naacl-main)
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| Challenge: | Existing approaches to learning semantically meaningful sentence embeddings are limited by the complexity of pre-trained models. |
| Approach: | They propose a sentence embedding learning approach that exploits both visual and textual information via a multimodal contrastive objective. |
| Outcome: | The proposed approach improves the state-of-the-art average Spearman’s correlation by 1.7% on a variety of semantic textual similarity tasks. |
Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning (2020.emnlp-main)
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| Challenge: | a recent study shows that humans are not supervised by the natural language inference . |
| Approach: | They propose to solve the natural language inference problem via task-agnostic multimodal pretraining. |
| Outcome: | The proposed network outperforms fully-supervised BiLSTM and BiLS+ELMO on plain text inference datasets. |
Tutorial on Multimodal Machine Learning (2022.naacl-tutorials)
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| Challenge: | Multimodal machine learning is a challenging but crucial area with numerous applications in multimedia, affective computing, robotics, finance, HCI, and healthcare. |
| Approach: | This tutorial will describe an updated taxonomy on multimodal machine learning synthesizing its core technical challenges and major directions for future research. |
| Outcome: | The proposed taxonomy synthesizes the core technical challenges and major directions for future research. |