  Our goal is to better comprehend procedural text, e.g., a paragraph about
photosynthesis, by not only predicting what happens, but why some actions need
to happen before others. Our approach builds on a prior process comprehension
framework for predicting actions' effects, to also identify subsequent steps
that those effects enable. We present our new model (XPAD) that biases effect
predictions towards those that (1) explain more of the actions in the paragraph
and (2) are more plausible with respect to background knowledge. We also extend
an existing benchmark dataset for procedural text comprehension, ProPara, by
adding the new task of explaining actions by predicting their dependencies. We
find that XPAD significantly outperforms prior systems on this task, while
maintaining the performance on the original task in ProPara. The dataset is
available at http://data.allenai.org/propara

Computation and Language;Artificial Intelligence