Towards Flow Graph Prediction of Open-Domain Procedural Texts

Abstract

Comprehension of procedural texts by machines is essential for reasoning about the steps in the texts and automating the procedures by robots. Previous work has focused on the cooking domain and proposed a recipe flow graph (r-FG) to represent an understanding of recipe texts with annotations. r-FG is defined as a directed acyclic graph with expressions related to procedures as nodes and the relationships between the nodes as edges. Previous work has proposed a framework that predicts r-FG representations in two steps: node prediction and edge prediction. While such advances have developed, the idea has only been applied to the cooking domain. This work proposes a wikiHow flow graph (w-FG) to represent an understanding of open-domain procedural texts. w-FG is compatible with r-FG, and the existing r-FG annotations in the cooking domain can be automatically converted into those in w-FG. We introduce a novel dataset called the w-FG corpus from wikiHow articles to evaluate flow graph prediction accuracy in domains other than cooking. Experimental results show that domain adaptation from the cooking to the target domain enables predictions of nodes with more than 75.0% accuracy and edges with more than 61.8%.