@InProceedings{fitzgerald-EtAl:2018:Long,
  author    = {FitzGerald, Nicholas  and  Michael, Julian  and  He, Luheng  and  Zettlemoyer, Luke},
  title     = {Large-Scale QA-SRL Parsing},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {2051--2060},
  abstract  = {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\% and span-level accuracy of 77.6\% (under human evaluation) on the full pipelined QA-SRL prediction task. They can also, as we show, be used to gather additional annotations at low cost.},
  url       = {http://www.aclweb.org/anthology/P18-1191}
}