Task semantics can be expressed by a set of input-to-output examples or a
piece of textual instruction. Conventional machine learning approaches for
natural language processing (NLP) mainly rely on the availability of
large-scale sets of task-specific examples. Two issues arise: first, collecting
task-specific labeled examples does not apply to scenarios where tasks may be
too complicated or costly to annotate, or the system is required to handle a
new task immediately; second, this is not user-friendly since end-users are
probably more willing to provide task description rather than a set of examples
before using the system. Therefore, the community is paying increasing interest
in a new supervision-seeking paradigm for NLP: learning from task instructions.
Despite its impressive progress, there are some common issues that the
community struggles with. This survey paper tries to summarize the current
research on instruction learning, particularly, by answering the following
questions: (i) what is task instruction, and what instruction types exist? (ii)
how to model instructions? (iii) what factors influence and explain the
instructions' performance? (iv) what challenges remain in instruction learning?
To our knowledge, this is the first comprehensive survey about textual
instructions.

此篇综述分析了自然语言处理中基于任务指导的学习，包括任务指导的含义、类型、建模方法、影响因素、表现和挑战。此外，该文是第一次关于文本指导的全面综述。