I2SRM: Intra- and Inter-Sample Relationship Modeling for Multimodal Information Extraction
This addresses multimodal named entity recognition and relation extraction for social media data, presenting an incremental improvement with competitive results.
The paper tackled multimodal information extraction by proposing the I2SRM method, which models intra- and inter-sample relationships, achieving F1-scores of 77.12% on Twitter-2015, 88.40% on Twitter-2017, and 84.12% on MNRE.
Multimodal information extraction is attracting research attention nowadays, which requires aggregating representations from different modalities. In this paper, we present the Intra- and Inter-Sample Relationship Modeling (I2SRM) method for this task, which contains two modules. Firstly, the intra-sample relationship modeling module operates on a single sample and aims to learn effective representations. Embeddings from textual and visual modalities are shifted to bridge the modality gap caused by distinct pre-trained language and image models. Secondly, the inter-sample relationship modeling module considers relationships among multiple samples and focuses on capturing the interactions. An AttnMixup strategy is proposed, which not only enables collaboration among samples but also augments data to improve generalization. We conduct extensive experiments on the multimodal named entity recognition datasets Twitter-2015 and Twitter-2017, and the multimodal relation extraction dataset MNRE. Our proposed method I2SRM achieves competitive results, 77.12% F1-score on Twitter-2015, 88.40% F1-score on Twitter-2017, and 84.12% F1-score on MNRE.