Unimodal and Multimodal Representation Training for Relation Extraction

• URL: http://arxiv.org/abs/2211.06168v1
• Date: Fri, 11 Nov 2022 12:39:35 GMT
• Title: Unimodal and Multimodal Representation Training for Relation Extraction
• Authors: Ciaran Cooney, Rachel Heyburn, Liam Maddigan, Mairead O'Cuinn, Chloe Thompson and Joana Cavadas
• Abstract summary: Multimodal integration of text, layout and visual information has achieved SOTA results in visually rich document understanding (VrDU) tasks, including relation extraction (RE) Here, we demonstrate the value of shared representations for RE tasks by conducting experiments in which each data type is iteratively excluded during training. While a bimodal text and layout approach performs best, we show that text is the most important single predictor of entity relations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal integration of text, layout and visual information has achieved SOTA results in visually rich document understanding (VrDU) tasks, including relation extraction (RE). However, despite its importance, evaluation of the relative predictive capacity of these modalities is less prevalent. Here, we demonstrate the value of shared representations for RE tasks by conducting experiments in which each data type is iteratively excluded during training. In addition, text and layout data are evaluated in isolation. While a bimodal text and layout approach performs best (F1=0.684), we show that text is the most important single predictor of entity relations. Additionally, layout geometry is highly predictive and may even be a feasible unimodal approach. Despite being less effective, we highlight circumstances where visual information can bolster performance. In total, our results demonstrate the efficacy of training joint representations for RE.

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