Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Abby Vander Linden, Brittany Harding, Brad Huang, Peter Clark and Christopher D. Manning
EMNLP 2014.
[Best paper award]


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 paper, we focus on a new reading comprehension task that requires complex reasoning over a single document. The input is a paragraph describing a biological process, and the goal is to answer questions that require an understanding of the relations 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 improves accuracy over baselines that use shallower representations.


Bib Entry

  author = {Jonathan Berant and Vivek Srikumar and Pei-Chun Chen
    and Abby Vander Linden and Brittany Harding
    and Brad Huang Peter Clark and Christopher D. Manning},
  booktitle = {Proceedings of EMNLP},
  title = {Modeling Biological Processes for Reading Comprehension},
  year = {2014},