Towards Flow Graph Prediction of Open-Domain Procedural Texts
Keisuke Shirai Hirotaka Kameko Shinsuke Mori
Machine comprehension of procedural texts is essential for reasoning about
the steps and automating the procedures. However, this requires identifying
entities within a text and resolving the relationships between the entities.
Previous work focused on the cooking domain and proposed a framework to convert
a recipe text into a flow graph (FG) representation. In this work, we propose a
framework based on the recipe FG for flow graph prediction of open-domain
procedural texts. To investigate flow graph prediction performance in
non-cooking domains, we introduce the wikiHow-FG corpus from articles on
wikiHow, a website of how-to instruction articles. In experiments, we consider
using the existing recipe corpus and performing domain adaptation from the
cooking to the target domain. Experimental results show that the domain
adaptation models achieve higher performance than those trained only on the
cooking or target domain data.