AI2D-RST: A multimodal corpus of 1000 primary school science diagrams

TL;DR

AI2D-RST is a comprehensive multimodal dataset of 1000 primary school science diagrams with detailed annotations of their structure, designed to advance research in automatic diagram understanding and multimodal reasoning.

Contribution

It introduces a new multi-layer annotation schema for diagrams, enriching the existing dataset with detailed multimodal and discourse annotations.

Findings

Rich, expert-annotated multimodal diagram corpus

Supports research in diagram understanding and reasoning

Provides a publicly available resource for education and AI

Abstract

This article introduces AI2D-RST, a multimodal corpus of 1000 English-language diagrams that represent topics in primary school natural sciences, such as food webs, life cycles, moon phases and human physiology. The corpus is based on the Allen Institute for Artificial Intelligence Diagrams (AI2D) dataset, a collection of diagrams with crowd-sourced descriptions, which was originally developed to support research on automatic diagram understanding and visual question answering. Building on the segmentation of diagram layouts in AI2D, the AI2D-RST corpus presents a new multi-layer annotation schema that provides a rich description of their multimodal structure. Annotated by trained experts, the layers describe (1) the grouping of diagram elements into perceptual units, (2) the connections set up by diagrammatic elements such as arrows and lines, and (3) the discourse relations between diagram elements, which are described using Rhetorical Structure Theory (RST). Each annotation layer in AI2D-RST is represented using a graph. The corpus is freely available for research and teaching.