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
Language:
URL:
https://aclanthology.org/P18-1191
DOI:
10.18653/v1/P18-1191
Bibkey:
Cite (ACL):
Nicholas FitzGerald, Julian Michael, Luheng He, and Luke Zettlemoyer. 2018. Large-Scale QA-SRL Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2051–2060, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Large-Scale QA-SRL Parsing (FitzGerald et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1191.pdf
Note:
 P18-1191.Notes.pdf
Presentation:
 P18-1191.Presentation.pdf
Video:
 https://vimeo.com/285805232
Code
 uwnlp/qasrl-bank +  additional community code
Data
QA-SRL Bank 2.0BioQA-SRL