Large-Scale QA-SRL Parsing
TL
TL;DR
A new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA- SRL parser are presented, and neural models for two QA -SRL subtasks are presented: detecting argument spans for a predicate and generating questions to label the semantic relationship.
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.
References32 items
1
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
2
CROWD-IN-THE-LOOP: A Hybrid Approach for Annotating Semantic Roles
3
A Joint Sequential and Relational Model for Frame-Semantic Parsing
4
Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension
5