Multi-modal Contrastive Representation Learning for Entity Alignment (2022.coling-1)
Copied to clipboard
| Challenge: | Existing studies focus on how to utilize information from different modalities, but it is not trivial to leverage multi-modal knowledge in entity alignment because of the modality heterogeneity. |
| Approach: | They propose a Multi-modal Contrastive Learning based Entity Alignment model which learns multiple individual representations from multiple modalities and performs contrastive learning to jointly model inter-modal and inter-modal interactions. |
| Outcome: | The proposed model outperforms state-of-the-art models on public datasets under both supervised and unsupervised conditions. |
Similar Papers
Multi-Modal Entities Matter: Benchmarking Multi-Modal Entity Alignment (2025.coling-main)
Copied to clipboard
| Challenge: | Existing MMEA datasets consider multi-modal data as attributes of textual entities, neglecting correlations between the multi-modal data. |
| Approach: | They propose a multi-modal entity alignment dataset that models multi-dimensional data as textual entities in the MMKG. |
| Outcome: | The proposed dataset can learn the structural information of entities by considering both intra-modal and cross-modal relations and infer the similarity of different types of entity pairs. |
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment (2024.lrec-main)
Copied to clipboard
| Challenge: | Current work on multi-modal semantic understanding primarily exploits a dual-encoder structure to separate image and text, but fails to learn cross-modal feature alignment. |
| Approach: | They propose a CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment by projecting features from different modalities into a unified deep space. |
| Outcome: | The proposed model outperforms baseline models on sarcasm detection and sentiment analysis tasks and is simple to implement without using task-specific external knowledge. |
Exploring the Impacts of Feature Fusion Strategy in Multi-modal Entity Alignment (2025.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to merge multi-modal knowledge only use one fusion strategy . however, the impact of the fusion on individual entities could be ignored . |
| Approach: | They propose an adaptive multi-modal feature fusion strategy for entity alignment that selects the optimal entity-level feature blending strategy. |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance compared to models using the same modality on a dataset with multiple inconsistent images and styles. |
Progressively Modality Freezing for Multi-Modal Entity Alignment (2024.acl-long)
Copied to clipboard
| Challenge: | Multi-Modal Entity Alignment aims to discover identical entities across heterogeneous knowledge graphs. |
| Approach: | They propose a strategy of progressive modality freezing that focuses on alignment-relevant features and enhances multi-modal feature fusion. |
| Outcome: | The proposed approach demonstrates state-of-the-art performance and the rationale for freezing modalities. |
Entity-level Cross-modal Learning Improves Multi-modal Machine Translation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-modal machine translation aims at improving translation performance by incorporating visual information. |
| Approach: | They propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation by combining a translation task and a reconstruction task. |
| Outcome: | The proposed approach achieves comparable or even better performance than state-of-the-art models. |
Noise-powered Multi-modal Knowledge Graph Representation Framework (2025.coling-main)
Copied to clipboard
| Challenge: | Current efforts to integrate MMKG with pretraining are scarce. |
| Approach: | They propose a method that integrates multi-modal entity features into MMKGs using a Transformer-based architecture equipped with modality-level noise masking. |
| Outcome: | The proposed method achieves SOTA performance across ten datasets. |
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis (2022.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework surpasses state-of-the-art methods on two public datasets. |
Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)
Copied to clipboard
| Challenge: | Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs). |
| Approach: | They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets. |
Capturing Latent Modal Association For Multimodal Entity Alignment (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for multimodal entity alignment overlook the quality of input modality embeddings during modality interaction, amplifying noise propagation while suppressing discriminative feature representations. |
| Approach: | They propose a model for capturing latent modal association for multimodal entity alignment using a self-attention mechanism to enhance salient information while attenuating noise within individual modality embeddings. |
| Outcome: | The proposed model achieves an absolute 3.1% higher Hits@1 score than the sota method. |
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction. |
| Approach: | They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples. |
| Outcome: | The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction. |