Towards Flow Graph Prediction of Open-Domain Procedural Texts (2023)

Abstract: 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.

## introduction a procedural text guides a human to complete daily activities like cooking and furniture assembly. machine comprehension of these texts is essential for reasoning about the steps @xcite and automating the procedures @xcite . however, it needs to identify entities within a text and resolve relationships between the entities. converting the text into an actionable representation (e.g., flow graph @xcite ) is an approach for solving these problems. there are several works on converting a procedural text into an action graph @xcite @xcite . in the cooking domain, various approaches @xcite @xcite have been taken because there are a rich amount of available resources on the web. among them, recipe flow graph (fg) (mori et al., 2014) has the advantage of capturing finegrained relationships at entity-level. while the original work @xcite introduced a framework and corpus in japanese, the recent work @xcite proposed those in english. the current fg framework has two issues. since fg is designed to represent the flow of actions in a procedural text, it should be applicable to other procedural domains such as crafting. one is that the framework has only been applied to the cooking domain. the other is that preparing a large number of annotations (e.g., thousands of articles) is unrealistic due to its complex annotation procedures. in this work, we propose a framework based on the english recipe flow graph (english-fg) @xcite for fg prediction of open- tag meaning c (f) component (food) t tool d duration q quantity ae (ac) action by expert (chef) ae2 (ac2) discontinuous ae (ac) ac (af) action by component (food) at action by tool sc (sf) state of component (food) st state of tool in experiments, we assume a low-resource scenario in which we can access only a few training examples in the target domain. this is a realistic scenario considering the huge annotation cost for fg. to tackle this issue, we consider domain adaptation from the existing cooking domain to the target domain. experimental results show that domain adaptation models obtain higher performance than those trained only on the cooking or target domain data. we also considered two data augmentation techniques to boost performance. from the results, we found that they improve performance in particular domains. our contributions are three-fold: ## recipe flow graph in this section, we provide a brief description of the recipe flow graph (fg) @xcite . a recipe fg is a directed acyclic graph g(v, e), where v represents entities as nodes, while e represents the relationships between the nodes as labeled edges. currently, fg annotations are available in japanese @xcite and english @xcite , and these corpora provide annotations of hundreds of recipes. note that japanese and english frameworks for fg are not compatible since the english fg uses additional tags to handle english-specific expressions. as we focus on texts in english, we consider the english-fg framework @xcite in the following sections. ## flow graph prediction of open-domain procedural texts our framework is based on the english-fg and applies to non-cooking domains by treating foods in recipe texts as final product components. examples of the components include tomato and beef for cooking, cardboard and glue for crafting, and gear and tire for vehicle maintenance. the framework uses tags and labels defined in ## wikihow-fg corpus the wikihow-fg corpus is a new flow graph corpus from articles on wikihow foot_1 , a website with more than 110k how-to articles. wikihow articles have been used as a language resource for procedural texts @xcite @xcite . in the following, we describe the data collection, annotation procedure, and statistics of the annotation results. ## conclusion we proposed a framework based on the english-fg and investigated flow graph prediction performance in non-cooking domains. we presented the wikihow-fg corpus from wikihow articles. we considered domain adaptation from the cooking to the target domain. experimental results show that domain adaptation models outperform those trained only on the cooking or target domain data. in future work, we consider applying this framework to other domains, such as material science and biochemistry. one can also try improving performance using more sophisticated data augmentation techniques. we hope that our work will provide new insights into procedural text understanding.