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Multimodal information extraction is attracting research attention nowadays,which requires aggregating representations from different modalities. In thispaper, we present the Intra- and Inter-Sample Relationship Modeling (I2SRM)method for this task, which contains two modules. Firstly, the intra-samplerelationship modeling module operates on a single sample and aims to learneffective representations. Embeddings from textual and visual modalities areshifted to bridge the modality gap caused by distinct pre-trained language andimage models. Secondly, the inter-sample relationship modeling module considersrelationships among multiple samples and focuses on capturing the interactions.An AttnMixup strategy is proposed, which not only enables collaboration amongsamples but also augments data to improve generalization. We conduct extensiveexperiments on the multimodal named entity recognition datasets Twitter-2015and Twitter-2017, and the multimodal relation extraction dataset MNRE. Ourproposed method I2SRM achieves competitive results, 77.12% F1-score onTwitter-2015, 88.40% F1-score on Twitter-2017, and 84.12% F1-score on MNRE.

I2SRM: Intra- and Inter-Sample Relationship Modeling for Multimodal Information Extraction