Large-Scale QA-SRL Parsing

Nicholas FitzGerald, Julian Michael, Luheng He, Luke Zettlemoyer


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.
Anthology ID:
P18-1191
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2051–2060
URL:
https://www.aclweb.org/anthology/P18-1191
DOI:
10.18653/v1/P18-1191
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Note:
 P18-1191.Notes.pdf
Video:
 https://vimeo.com/285805232
Presentation:
 P18-1191.Presentation.pdf