Modeling Biological Processes for Reading Comprehension

Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this pa-per, we focus on a new reading compre-hension task that requires complex reason-ing over a single document. The input is a paragraph describing a biological pro-cess, and the goal is to answer questions that require an understanding of the re-lations between entities and events in the process. To answer the questions, we first predict a rich structure representing the process in the paragraph. Then, we map the question to a formal query, which is executed against the predicted structure. We demonstrate that answering questions via predicted structures substantially im-proves accuracy over baselines that use shallower representations. 1