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.

本文提出一种基于标记跨度和跨度之间关系的统一格式的思想，成功应用于语法、语义、信息内容，情感等多种自然语言处理任务，其结构化的方法能将不同任务转化为相同的格式，同时得到较高的性能表现，并提出多任务学习的优势。最后作者将各数据转换为统一格式来建立一个基准测试，提供了一个综合的测试平台，用于评估未来模型对于通用自然语言处理的表现。