  Procedural texts often describe processes (e.g., photosynthesis and cooking)
that happen over entities (e.g., light, food). In this paper, we introduce an
algorithm for procedural reading comprehension by translating the text into a
general formalism that represents processes as a sequence of transitions over
entity attributes (e.g., location, temperature). Leveraging pre-trained
language models, our model obtains entity-aware and attribute-aware
representations of the text by joint prediction of entity attributes and their
transitions. Our model dynamically obtains contextual encodings of the
procedural text exploiting information that is encoded about previous and
current states to predict the transition of a certain attribute which can be
identified as a span of text or from a pre-defined set of classes. Moreover,
our model achieves state of the art results on two procedural reading
comprehension datasets, namely ProPara and npn-cooking
