Browse QA-SRL + QANom

We are a group of researchers spanning the University of Washington, Bar-Ilan University, Facebook AI Research, and the Allen Institute for Artificial Intelligence. Our goal is to advance the state of the art in broad-coverage natural language understanding. We believe the way forward is with new datasets that are:

• Crowdsourced: modern machine learning methods require big training sets, which means scalability is a top priority.
• Richly structured: in order to improve over powerful representations learned from unlabeled data, we need strong, structured supervision signal.
• Extensible: annotation schemas should be flexible enough to accommodate new semantic phenomena without requiring expensive rounds of reannotation or brittle postprocessing rules.

Our research explores a variety of points in the design space spanned by these criteria. The common feature between our projects is using natural language to annotate natural language. This results in interpretable structures that can be annotated by non-experts at scale, which have the further advantage of being agnostic to choices of linguistic formalism.