Work

Large-Scale QA-SRL Parsing

Year: 2018

Type: conference-paper

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.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p18-1191

Authors: Nicholas FitzGerald, Julian Michael, Luheng He, Luke Zettlemoyer

Institutions: Google (United States), University of Washington

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Allen Institute for Artificial Intelligence, Army Research Office

Awards: CAREER: Learning Scalable Models for Grounded Semantic Parsing, G657448715, G7452299184, RI: Medium: Broad-Coverage Semantic Parsing: Linguistic Representation Learning from Crowd-Scale Data