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
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/P18-1191.pdf
- Code
- uwnlp/qasrl-bank + additional community code
- Data
- QA-SRL Bank 2.0, Bio, QA-SRL