  In this work we design a narrative understanding tool Text2ALM. This tool
uses an action language ALM to perform inferences on complex interactions of
events described in narratives. The methodology used to implement the Text2ALM
system was originally outlined by Lierler, Inclezan, and Gelfond (2017) via a
manual process of converting a narrative to an ALM model. It relies on a
conglomeration of resources and techniques from two distinct fields of
artificial intelligence, namely, natural language processing and knowledge
representation and reasoning. The effectiveness of system Text2ALM is measured
by its ability to correctly answer questions from the bAbI tasks published by
Facebook Research in 2015. This tool matched or exceeded the performance of
state-of-the-art machine learning methods in six of the seven tested tasks. We
also illustrate that the Text2ALM approach generalizes to a broader spectrum of
narratives.
