Can images help recognize entities? A study of the role of images for Multimodal NER
This work addresses the problem of effectively integrating visual context for MNER, which is incremental as it analyzes existing methods rather than proposing new ones.
The study analyzed multimodal named entity recognition (MNER) to understand when images improve performance, finding that adding images does not always boost results and that captions can be beneficial in certain scenarios.
Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context. While many multimodal neural techniques have been proposed to incorporate images into the MNER task, the model's ability to leverage multimodal interactions remains poorly understood. In this work, we conduct in-depth analyses of existing multimodal fusion techniques from different perspectives and describe the scenarios where adding information from the image does not always boost performance. We also study the use of captions as a way to enrich the context for MNER. Experiments on three datasets from popular social platforms expose the bottleneck of existing multimodal models and the situations where using captions is beneficial.