Papers with fusion strategies
Ambiguity-aware Multi-level Incongruity Fusion Network for Multi-Modal Sarcasm Detection (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection focus on fusing text and image information to establish cross-modal correlations, overlooking the significance of original unimodal incongruity information. |
| Approach: | They propose a multi-modal incongruity learning module to capture inconcluity information simultaneously at the text-level, image-level and cross-modal-level. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a publicly available dataset. |
Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection (2022.coling-1)
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| Challenge: | Existing systems that focus on persona do not explore well the correlation between persona and empathy. |
| Approach: | They propose a suite of fusion strategies that capture interaction between persona, emotion, and entailment information of the utterances. |
| Outcome: | The proposed model outperforms the previous methods by 2.3% on original personas and 1.9% on revised persona models in terms of hits@1 accuracy. |
Multimodal Quality Estimation for Machine Translation (2020.acl-main)
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| Challenge: | Existing work has only explored textual context. |
| Approach: | They propose to use visual and text modalities to explore Quality Estimation for Machine Translation and integrate them into multimodal QE frameworks. |
| Outcome: | The proposed approaches improve on sentence-level and document-level predictions using visual features extracted from images. |
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment (P18-1)
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| Challenge: | Existing approaches to classify human affect and subjective information from multiple data sources are limited by the lack of high-level feature associations. |
| Approach: | They propose a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on published datasets and visualizes and interprets synchronized attention over modalities. |
Uncertainty-Guided Modal Rebalance for Hateful Memes Detection (2024.acl-long)
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| Challenge: | Existing methods for integrating hate information from different modalities ignore the modality uncertainty caused by the contribution degree of each modality to hate sentiment. |
| Approach: | They propose an Uncertainty-guided Modal Rebalance framework for hateful memes detection . they propose to combine cross-modal fusion features with unimodal features . |
| Outcome: | The proposed framework produces state-of-the-art performance on four widely-used datasets. |
MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown impressive progress in mathematical problem-solving . current approaches to enhance mathematical reasoning focus on instance-level modifications . |
| Approach: | They propose a framework that enhances mathematical reasoning through cross-problem instruction synthesis. |
| Outcome: | The proposed framework boosts mathematical reasoning by 18.0 points while maintaining high data efficiency. |
Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos (2022.emnlp-main)
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| Challenge: | Existing multimodal summarization methods are limited to monolingual videos . a proposed task aims to generate cross-lingual summaries from multimodal inputs . |
| Approach: | They propose a task to generate cross-lingual summaries from multimodal inputs of videos . they propose fusion network that integrates multimodal and cross-linguistic information . |
| Outcome: | The proposed task outperforms existing methods on a reorganized How2 dataset on the reorganized How2 data set. |
What to Fuse and How to Fuse: Exploring Emotion and Personality Fusion Strategies for Explainable Mental Disorder Detection (2023.findings-acl)
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| Challenge: | Mental health disorders (MHD) are one of the greatest challenges facing our healthcare systems and modern societies in general. |
| Approach: | They integrate and extend the research by conducting extensive experiments with three types of deep learning-based fusion strategies: feature-level fusion, model fusion and task fusion. |
| Outcome: | The proposed techniques show that they can be used to improve mental health detection from textual data. |
Latent Distribution Decouple for Uncertain-Aware Multimodal Multi-label Emotion Recognition (2025.findings-acl)
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Jingwang Huang, Jiang Zhong, Qin Lei, Gaojinpeng Gaojinpeng, Ymyang Ymyang, Sirui Wang, PeiguangLi PeiguangLi, Kaiwen Wei
| Challenge: | Existing studies focus on improving fusion strategies and modeling modality-to-label dependencies, but they overlook the impact of aleatoric uncertainty, which is inherent noise in multimodal data. |
| Approach: | They propose a latent emotional distribution decomposition with uncertainty perception framework to model aleatoric uncertainty in multimodal data. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the CMU-MOSEI and M3ED datasets, highlighting the importance of uncertainty modeling in MMER. |
A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis (2026.findings-acl)
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Muhammad Arslan Manzoor, Dilshod Azizov, Daniil Orel, Umer Siddique, Zain Muhammad Mujahid, Yufang Hou, Preslav Nakov
| Challenge: | a large-scale label set for media outlets from Media Bias/Fact Check (MBFC) is lacking in the field. |
| Approach: | They propose to use a large-scale label set to analyze outlets' representations . they also propose to evaluate embedding views and fusion strategies . |
| Outcome: | The proposed method achieves state-of-the-art results on ACL-2020 and establishes strong benchmarks on MBFC-2025. |
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)
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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) is crucial for large language models without costly retraining. |
| Approach: | They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer. |
| Outcome: | The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer. |