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

我们提出了一种神经机器阅读模型，它从过程文本中构建动态知识图，并利用它们来跟踪参与实体的演变状态，从而改善机器对文本的理解。