Vivek Srikumar

Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Abby Vander Linden, Brittany Harding, Brad Huang, Peter Clark and Christopher D. Manning

Proceedings of the Conference on Empirical Methods in Natural Language Processing (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.

@inproceedings{berant2014modeling,
  author = {Berant, Jonathan and Srikumar, Vivek and Chen, Pei-Chun and Vander Linden, Abby and Harding, Brittany and Huang, Brad and Clark, Peter and Manning, Christopher D.},
  title = {{Modeling Biological Processes for Reading Comprehension}},
  booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2014}
}