Challenge: Existing methods to perform multimodal information extraction only investigated entity-based tasks under supervised learning with adequate labeled data.
Approach: They propose to investigate the entity-based MIE tasks under the low-resource settings by decomposing the features into image, entity, and context factors.
Outcome: The proposed method is able to perform on two public MIE benchmark datasets and the experimental results confirm it.

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MRE-MI: A Multi-image Dataset for Multimodal Relation Extraction in Social Media Posts (2025.findings-naacl)

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Challenge: Existing approaches to Multimodal Relation Extraction focus on single image scenarios . current approaches focus on text paired with a single image, ignoring valuable insights provided by remaining images.
Approach: They propose a human-annotated dataset that includes multi-image and single-image instances for relation extraction.
Outcome: The proposed model significantly improves relation extraction in multi-image scenarios.
ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos (2023.emnlp-main)

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Challenge: despite its importance, there are few datasets that cover multimodal counterfactual reasoning . a dataset focusing on this area is limited because of its limited coverage over synthetic environments .
Approach: They develop a video question answering dataset that provides questions on multimodal reasoning . they ask questions about counterfactual hypotheses over visual events .
Outcome: The proposed dataset shows a significant performance gap between models and humans . it provides questions that span physical, social, and temporal dimensions .
Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis (2023.acl-short)

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Challenge: Existing retrieval-augmented approaches focus on modeling the retrieved textual knowledge but this may not be able to accurately identify complex relations.
Approach: They propose to retrieve multimodal relation extraction information based on object, sentence, and whole image . they propose to synthesize the object-level, image-level and sentence-level information .
Outcome: The proposed method outperforms state-of-the-art models on multimodal relation extraction.
Rethinking Multimodal Entity and Relation Extraction from a Translation Point of View (2023.acl-long)

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Challenge: Special attention is paid to the cross-modal misalignment in text-image datasets which may mislead the learning.
Approach: They propose a multimodal back-translation method which uses diffusion-based generative models for pseudo-paralleled pairs and a divergence estimator to construct a high-resource corpora as a bridge for low-ressource learners.
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Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition (2020.emnlp-main)

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Challenge: Using labeled data, named entity recognition is labor-intensive, time-consuming and expensive.
Approach: They propose a method which decomposes named entity into two parts: entity and context.
Outcome: The proposed method improves the generalization ability of models under limited observational examples.
Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing (2025.findings-emnlp)

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Challenge: Multimodal Large Language Models (MLLMs) often rely on spurious correlations, undermining their robustness and generalization.
Approach: They propose a causal mediation-based debiasing framework to address correlation bias in MLLMs . they distinguish core semantics from spurious textual and visual contexts using counterfactual examples .
Outcome: The proposed framework surpasses existing state-of-the-art models on sarcasm detection and sentiment analysis tasks.
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling (2023.acl-long)

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Challenge: Existing research on multimodal relation extraction (MRE) faces internal-information over-utilization and external-information under-exploitation.
Approach: They propose a framework that implements internal-information screening and external-information exploiting to address these challenges.
Outcome: The proposed framework outperforms the current best model on the benchmark dataset.
MNER-MI: A Multi-image Dataset for Multimodal Named Entity Recognition in Social Media (2024.lrec-main)

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Challenge: Recent research has focused on multimodal named entity recognition (MNER) but current approaches focus on text and a single accompanying image, leaving a significant research gap in multi-image scenarios.
Approach: They propose to construct a human-annotated MNER dataset with multiple images called MNER-MI and a temporal prompt model with multiple image to address the new challenges in multi-image scenarios.
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OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets (2026.eacl-industry)

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Challenge: Multimodal Large Language Models (MLLMs) are used for document information extraction, but their impact on document information processing remains unclear.
Approach: They propose an automated hierarchical error analysis framework that leverages large language models to diagnose errors systematically.
Outcome: The proposed framework can achieve comparable performance to OCR-enhanced approaches.
ImaRA: An Imaginative Frame Augmented Method for Low-Resource Multimodal Metaphor Detection and Explanation (2025.findings-naacl)

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Challenge: Existing methods for multimodal metaphor detection neglect cross-domain and attribute similarity characteristics underlying multimodal understanding.
Approach: They propose an Imaginative FRame Augmented method for multimodal metaphor detection and explanation . they use a cross-modal imagination dataset rich in multimodal multimodal expressions .
Outcome: The proposed method outperforms existing methods with training data on two datasets.

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