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.

本文提出了一种基于食谱流程图的框架，来预测开放领域中流程文本的流程图，通过实现领域适应模型，实验结果表明领域适应的模型比只在烹饪或目标领域数据上训练的模型具有更高的性能。