Modeling biological processes for reading comprehension

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationEMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages1499-1510
Number of pages12
ISBN (Electronic)9781937284961
DOIs
StatePublished - 2014
Externally publishedYes
Event2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 - Doha, Qatar
Duration: 25 Oct 201429 Oct 2014

Publication series

NameEMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

Conference2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014
Country/TerritoryQatar
CityDoha
Period25/10/1429/10/14

Fingerprint

Dive into the research topics of 'Modeling biological processes for reading comprehension'. Together they form a unique fingerprint.

Cite this