Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources

Understand

In order to extract event information from text, a machine reading model must learn to accurately read and interpret the ways in which that information is expressed.

• But it must also, as the human reader must, aggregate numerous individual value hypotheses into a single coherent global analysis, applying global constraints which reflect prior knowledge of the domain.
• In this work we focus on the task of extracting plane crash event information from clusters of related news articles whose labels are derived via distant supervision.
• Unlike previous machine reading work, we assume that while most target values will occur frequently in most clusters, they may also be missing or incorrect.