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.
@article{arxiv.1805.05377,
title = {Large-Scale QA-SRL Parsing},
author = {Nicholas FitzGerald and Julian Michael and Luheng He and Luke Zettlemoyer},
journal= {arXiv preprint arXiv:1805.05377},
year = {2018}
}
Comments
10 pages, 3 figures, 8 tables. Accepted to ACL 2018