Abstract
We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A qualitative analysis demonstrates that the crowd-generated questionanswer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, Nom- Bank, and QA-SRL) along with many previously under-resourced ones, including implicit arguments and relations. We also report baseline models for question generation and answering, and summarize a recent approach for using QAMR labels to improve an Open IE system. These results suggest the freely available1 QAMR data and annotation scheme should support significant future work.
Original language | English |
---|---|
Title of host publication | Short Papers |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 560-568 |
Number of pages | 9 |
ISBN (Electronic) | 9781948087292 |
State | Published - 2018 |
Externally published | Yes |
Event | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States Duration: 1 Jun 2018 → 6 Jun 2018 |
Publication series
Name | NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference |
---|---|
Volume | 2 |
Conference
Conference | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 |
---|---|
Country/Territory | United States |
City | New Orleans |
Period | 1/06/18 → 6/06/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computational Linguistics.