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In this work we design a narrative understanding tool Text2ALM. This tooluses an action language ALM to perform inferences on complex interactions ofevents described in narratives. The methodology used to implement the Text2ALMsystem was originally outlined by Lierler, Inclezan, and Gelfond (2017) via amanual process of converting a narrative to an ALM model. It relies on aconglomeration of resources and techniques from two distinct fields ofartificial intelligence, namely, natural language processing and knowledgerepresentation and reasoning. The effectiveness of system Text2ALM is measuredby its ability to correctly answer questions from the bAbI tasks published byFacebook Research in 2015. This tool matched or exceeded the performance ofstate-of-the-art machine learning methods in six of the seven tested tasks. Wealso illustrate that the Text2ALM approach generalizes to a broader spectrum ofnarratives.

Information Extraction Tool Text2ALM: From Narratives to Action Language System Descriptions