  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.
