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We propose a neural machine-reading model that constructs dynamic knowledgegraphs from procedural text. It builds these graphs recurrently for each stepof the described procedure, and uses them to track the evolving states ofparticipant entities. We harness and extend a recently proposed machine readingcomprehension (MRC) model to query for entity states, since these states aregenerally communicated in spans of text and MRC models perform well inextracting entity-centric spans. The explicit, structured, and evolvingknowledge graph representations that our model constructs can be used indownstream question answering tasks to improve machine comprehension of text,as we demonstrate empirically. On two comprehension tasks from the recentlyproposed PROPARA dataset (Dalvi et al., 2018), our model achievesstate-of-the-art results. We further show that our model is competitive on theRECIPES dataset (Kiddon et al., 2015), suggesting it may be generallyapplicable. We present some evidence that the model's knowledge graphs help itto impose commonsense constraints on its predictions.

Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension