Linking entities across different modalities is a crucial task in natural language processing (NLP). However, it poses significant challenges, especially when the entity representations are not comprehensive or diverse enough. In this work, we aim to address these issues by proposing a novel framework called Dual-way Enhanced (DWE) for improving multimodal entity linking (MEL). Our approach enhances the query with refined multimodal information and enriches the semantics of entities by leveraging Wikipedia descriptions.
Enhancing Queries
In DWE, we employ pretrained visual encoders to obtain image representations and fine-grained visual attributes for each mention. These visual characteristics are then combined with textual features to create a unified representation of the query. This dual-way enhanced framework (i.e., text + image) allows us to capture both semantic and structural information, which improves the linking performance.
Enriching Entity Representations
To bridge the semantic gap between vision and text in the language model, we design three enhanced units based on the cross-modal enhancer. These units help to align and integrate visual and textual features effectively, leading to improved entity representations. By incorporating both modalities, our framework can better capture the context and nuances of entities, resulting in more accurate linkage.
Experimental Results
We conducted comparative experiments on various baseline models to evaluate the effectiveness of DWE. Our results show that DWE outperforms existing methods in terms of linking accuracy, demonstrating its superiority in enhancing entity representation for multimodal linking tasks.
Conclusion
In summary, DWE offers a novel approach to improving MEL by enriching entity representations with refined multimodal information and cross-modal alignment. By leveraging pretrained visual encoders and aligning textual and visual features, our framework can capture both semantic and structural cues, leading to improved linking performance. As the field of NLP continues to evolve, we believe that DWE will play a crucial role in bridging the gap between vision and language, enabling more accurate and robust multimodal entity linking.
