Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.18653/v1/P18-1191 | PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1 |
DocType | Volume | Citations |
Conference | abs/1805.05377 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicholas FitzGerald | 1 | 156 | 8.97 |
| julian michael | 2 | 78 | 5.08 |
| Luheng He | 3 | 190 | 10.77 |
| Luke S. Zettlemoyer | 4 | 3348 | 163.34 |