Fangsheng Weng


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RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER
Lin Sun | Jiquan Wang | Yindu Su | Fangsheng Weng | Yuxuan Sun | Zengwei Zheng | Yuanyi Chen
Proceedings of the 28th International Conference on Computational Linguistics

Multimodal named entity recognition (MNER) for tweets has received increasing attention recently. Most of the multimodal methods used attention mechanisms to capture the text-related visual information. However, unrelated or weakly related text-image pairs account for a large proportion in tweets. Visual clues unrelated to the text would incur uncertain or even negative effects for multimodal model learning. In this paper, we propose a novel pre-trained multimodal model based on Relationship Inference and Visual Attention (RIVA) for tweets. The RIVA model controls the attention-based visual clues with a gate regarding the role of image to the semantics of text. We use a teacher-student semi-supervised paradigm to leverage a large unlabeled multimodal tweet corpus with a labeled data set for text-image relation classification. In the multimodal NER task, the experimental results show the significance of text-related visual features for the visual-linguistic model and our approach achieves SOTA performance on the MNER datasets.