Paper Info
Title
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.
Abstract
For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation benchmark (GLUE), a tool for evaluating and analyzing the performance of models across a diverse range of existing NLU tasks. GLUE is model-agnostic, but it incentivizes sharing knowledge across tasks because certain tasks have very limited training data. We further provide a hand-crafted diagnostic test suite that enables detailed linguistic analysis of NLU models. We evaluate baselines based on current methods for multi-task and transfer learning and find that they do not immediately give substantial improvements over the aggregate performance of training a separate model per task, indicating room for improvement in developing general and robust NLU systems.
Year
Venue
DocType
2018
international conference on learning representations
Conference
Volume
Citations 
PageRank 
abs/1804.07461
58
1.33
References 
Authors
39
6
Name
Order
Citations
PageRank
Alex Wang1715.27
Amanpreet Singh21098.34
julian michael3785.08
Felix Hill434617.90
Omer Levy5138756.96
Samuel R. Bowman690644.99