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.

翻译：机器理解程序性文本对于推理步骤和自动化执行程序至关重要。然而，这需要识别文本中的实体并解析实体间的关系。先前工作聚焦于烹饪领域，提出了将食谱文本转化为流程图（FG）表示的框架。本研究基于食谱FG框架，提出面向开放领域程序性文本的流程图预测方法。为探究非烹饪领域的流程图预测性能，我们从wikiHow（一个包含操作指导文章的网站）构建了wikiHow-FG语料库。实验中，我们尝试使用现有食谱语料库并进行从烹饪领域到目标领域的域适应。实验结果表明，域适应模型比仅使用烹饪领域或目标领域数据训练的模型取得了更高性能。