Work

Crowdsourcing Question-Answer Meaning Representations

Year: 2018

Type: conference-paper

Abstract: Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, Luke Zettlemoyer. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.18653/v1/n18-2089

Authors: Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, Luke Zettlemoyer

Institutions: University of Washington, Allen Institute for Artificial Intelligence, Google (United States), Bar-Ilan University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Deutsche Forschungsgemeinschaft, Bar-Ilan University, Israel Science Foundation

Awards: CAREER: Learning Scalable Models for Grounded Semantic Parsing, G7805275943, G8132464742, RI: Medium: Broad-Coverage Semantic Parsing: Linguistic Representation Learning from Crowd-Scale Data