Learning from Task Descriptions

Understand

Typically, machine learning systems solve new tasks by training on thousands of examples.

• In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two.
• To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area.
• We instantiate this framework with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks.