Introducing QA-SRL Bank 2.0
The first large-scale QA-SRL dataset and high-quality parserExplore the data » Paper » Download » Model » Live Demo »
About the QA-SRL Project
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.
- Large-Scale QA-SRL Parsing won an Honorable Mention at ACL 2018!
- You can now browse the QA-SRL Bank 2.0 dataset online, or try a live demo of our model!
- We have released the first large-scale QA-SRL dataset and high-quality model! The paper, Large-Scale QA-SRL Parsing, will appear at ACL 2018.
- Julian and Gabi presented Crowdsourcing Question-Answer Meaning Representations and Supervised Open Information Extraction at NAACL 2018 in New Orleans.