  Pre-trained text-to-text transformers such as BART have achieved impressive
performance across a range of NLP tasks. Recent study further shows that they
can learn to generalize to novel tasks, by including task descriptions as part
of the source sequence and training the model with (source, target) examples.
At test time, these fine-tuned models can make inferences on new tasks using
the new task descriptions as part of the input. However, this approach has
potential limitations, as the model learns to solve individual (source, target)
examples (i.e., at the instance level), instead of learning to solve tasks by
taking all examples within a task as a whole (i.e., at the task level). To this
end, we introduce Hypter, a framework that improves text-to-text transformer's
generalization ability to unseen tasks by training a hypernetwork to generate
task-specific, light-weight adapters from task descriptions. Experiments on
ZEST dataset and a synthetic SQuAD dataset demonstrate that Hypter improves
upon fine-tuning baselines. Notably, when using BART-Large as the main network,
Hypter brings 11.3% comparative improvement on ZEST dataset.
