@inproceedings{ffa8df9b3c6a4d17b3146bb5cc2b1f78,
title = "Crowdsourcing question-answer meaning representations",
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.",
author = "Julian Michael and Gabriel Stanovsky and Luheng He and Ido Dagan and Luke Zettlemoyer",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics.; 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 ; Conference date: 01-06-2018 Through 06-06-2018",
year = "2018",
language = "الإنجليزيّة",
series = "NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "560--568",
booktitle = "Short Papers",
address = "الولايات المتّحدة",
}