The mainstream machine learning paradigms for NLP often work with two
underlying presumptions. First, the target task is predefined and static; a
system merely needs to learn to solve it exclusively. Second, the supervision
of a task mainly comes from a set of labeled examples. A question arises: how
to build a system that can keep learning new tasks from their instructions?
This work defines a new learning paradigm ConTinTin (Continual Learning from
Task Instructions), in which a system should learn a sequence of new tasks one
by one, each task is explained by a piece of textual instruction. The system is
required to (i) generate the expected outputs of a new task by learning from
its instruction, (ii) transfer the knowledge acquired from upstream tasks to
help solve downstream tasks (i.e., forward-transfer), and (iii) retain or even
improve the performance on earlier tasks after learning new tasks (i.e.,
backward-transfer). This new problem is studied on a stream of more than 60
tasks, each equipped with an instruction. Technically, our method
InstructionSpeak contains two strategies that make full use of task
instructions to improve forward-transfer and backward-transfer: one is to learn
from negative outputs, the other is to re-visit instructions of previous tasks.
To our knowledge, this is the first time to study ConTinTin in NLP. In addition
to the problem formulation and our promising approach, this work also
contributes to providing rich analyses for the community to better understand
this novel learning problem.

本研究提出了一种新的学习范式 ConTinTin (Continual Learning from Task Instructions)，通过学习一系列新任务，每个任务都有一条文本指令，要求系统生成预期输出并将前面任务中获取的知识转移到后续任务中，同时保持甚至提高先前任务的性能。在 60 个由文本指令装备的任务流中，本文方法 InstructionSpeak 具有学习负面输出和重新访问先前任务指令的两个策略，可充分利用任务指令提高前向和后向传递效果。