Work

JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation

Year: 2019

Type: conference-paper

Abstract: Rajas Agashe, Srinivasan Iyer, Luke Zettlemoyer. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d19-1546

Authors: Rajas Agashe, Srinivasan Iyer, Luke Zettlemoyer

Institution: University of Washington

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Army Research Office

Awards: CAREER: Learning Scalable Models for Grounded Semantic Parsing, G657448715, G7452299184, RI: Medium: Broad-Coverage Semantic Parsing: Linguistic Representation Learning from Crowd-Scale Data

OpenAlex ID: W2970004377

FWCI: 3.983

Cites: 36

Cited by: 48

Related to: 10