  Natural language processing covers a wide variety of tasks predicting syntax,
semantics, and information content, and usually each type of output is
generated with specially designed architectures. In this paper, we provide the
simple insight that a great variety of tasks can be represented in a single
unified format consisting of labeling spans and relations between spans, thus a
single task-independent model can be used across different tasks. We perform
extensive experiments to test this insight on 10 disparate tasks spanning
dependency parsing (syntax), semantic role labeling (semantics), relation
extraction (information content), aspect based sentiment analysis (sentiment),
and many others, achieving performance comparable to state-of-the-art
specialized models. We further demonstrate benefits of multi-task learning, and
also show that the proposed method makes it easy to analyze differences and
similarities in how the model handles different tasks. Finally, we convert
these datasets into a unified format to build a benchmark, which provides a
holistic testbed for evaluating future models for generalized natural language
analysis.
