Reducing the Bias of Visual Objects in Multimodal Named Entity Recognition
Xin Zhang, Jingling Yuan, Lin Li, Jianquan Liu
Abstract
Visual information shows to empower accurately named entity recognition in short texts, such as posts from social media. Previous work on multimodal named entity recognition (MNER) often regards an image as a set of visual objects, trying to explicitly align visual objects and entities. However, these methods may suffer the bias introduced by visual objects when they are not identical to entities in quantity and entity type. Different from this kind of explicit alignment, we argue that implicit alignment is effective in optimizing the shared semantic space learning between text and image for improving MNER. To this end, we propose a de-bias contrastive learning based approach for MNER, which studies modality alignment enhanced by cross-modal contrastive learning. Specifically, our contrastive learning adopts a hard sample mining strategy and a debiased contrastive loss to alleviate the bias of quantity and entity type, respectively, which globally learns to align the feature spaces from text and image. Finally, the learned semantic space works with a NER decoder to recognize entities in text. Conducted on two benchmark datasets, experimental results show that our approach outperforms the current state-of-the-art methods.