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We present a new dataset and models for comprehending paragraphs aboutprocesses (e.g., photosynthesis), an important genre of text describing adynamic world. The new dataset, ProPara, is the first to contain natural(rather than machine-generated) text about a changing world along with a fullannotation of entity states (location and existence) during those changes (81kdatapoints). The end-task, tracking the location and existence of entitiesthrough the text, is challenging because the causal effects of actions areoften implicit and need to be inferred. We find that previous models that haveworked well on synthetic data achieve only mediocre performance on ProPara, andintroduce two new neural models that exploit alternative mechanisms for stateprediction, in particular using LSTM input encoding and span prediction. Thenew models improve accuracy by up to 19%. The dataset and models are availableto the community at http://data.allenai.org/propara.

Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension