Large-Scale QA-SRL Parsing

05/14/2018

∙

by   Nicholas FitzGerald, et al.

∙

0

∙

share

We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6 accuracy of 77.6 prediction task. They can also, as we show, be used to gather additional annotations at low cost.