Papers with MCR
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution (2022.coling-1)
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Danfeng Guo, Arpit Gupta, Sanchit Agarwal, Jiun-Yu Kao, Shuyang Gao, Arijit Biswas, Chien-Wei Lin, Tagyoung Chung, Mohit Bansal
| Challenge: | Multimodal coreference resolution (MCR) is a crucial capability for building next-generation conversational agents. |
| Approach: | They propose a multimodal coreference resolution model that resolves coreferences made in multi-turn dialogues with scene images. |
| Outcome: | The proposed model resolves coreferences made in multi-turn dialogues with scene images. |
Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)
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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu
| Challenge: | Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes. |
| Approach: | They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence. |
| Outcome: | The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show. |
Answering Questions by Meta-Reasoning over Multiple Chains of Thought (2023.emnlp-main)
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| Challenge: | Modern systems for multi-hop question answering (QA) break questions into a sequence of reasoning steps, termed chain-of-thought (CoT) Often, multiple chains are sampled and aggregated, but the intermediate steps themselves are discarded. |
| Approach: | They propose a method which prompts large language models to meta-reason over multiple chains of thought rather than aggregate their answers. |
| Outcome: | The proposed approach outperforms baselines on 7 multi-hop QA datasets. |
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews (2021.acl-long)
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| Challenge: | Existing review helpfulness prediction tasks rely on text and image modalities to analyze review helpfuliness. |
| Approach: | They propose a task to analyze review helpfulness from text and visual modalities and propose 'multi-perspective coherent reasoning' method to combine coherence between product and review is proposed. |
| Outcome: | The proposed method can lead to performance increase of 8.5% compared to the best performing text-only model. |
Investigating Multilingual Coreference Resolution by Universal Annotations (2023.findings-emnlp)
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| Challenge: | Existing systems for multilingual coreference resolution have been challenging due to linguistic diversity and complexity of different languages. |
| Approach: | They propose a multilingual coreference dataset with universal morphosyntactic and coreference annotations. |
| Outcome: | The proposed dataset improves the baseline system by 0.9% . the proposed dataset is based on the framework of Universal Dependencies 2 . |
Multimodal Coreference Resolution for Chinese Social Media Dialogues: Dataset and Benchmark Approach (2025.acl-long)
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| Challenge: | Multimodal coreference resolution (MCR) aims to identify mentions referring to the same entity across different modalities, such as text and visuals. |
| Approach: | They propose a Chinese multimodal coreference dataset based on Douyin short-video platform to help researchers understand multimodal content. |
| Outcome: | The proposed dataset pairs short videos with corresponding textual dialogues from user comments and includes manually annotated coreference clusters for person mentions in the text and the coreferential person head regions in the corresponding video frames. |
Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition (2026.findings-acl)
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| Challenge: | Existing approaches to GMNER use MLLMs as auxiliary tools, causing cumulative error propagation and a lack of rigorous cross-modal verification. |
| Approach: | They propose a model that enforces structured cross-modal reasoning through Multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |
| Outcome: | The proposed model enforces structured cross-modal reasoning through multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |