PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains. (arXiv:2102.12206v3 [cs.CL] UPDATED)

Natural Language Processing algorithms have made incredible progress, but
they still struggle when applied to out-of-distribution examples. We address a
challenging and underexplored version of this domain adaptation problem, where
an algorithm is trained on several source domains, and then applied to examples
from unseen domains that are unknown at training time. Particularly, no
examples, labeled or unlabeled, or any other knowledge about the target domain
are available to the algorithm at training time. We present PADA: An
example-based autoregressive …

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