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Natural Language Processing algorithms have made incredible progress, butthey still struggle when applied to out-of-distribution examples. We address achallenging and underexplored version of this domain adaptation problem, wherean algorithm is trained on several source domains, and then applied to examplesfrom unseen domains that are unknown at training time. Particularly, noexamples, labeled or unlabeled, or any other knowledge about the target domainare available to the algorithm at training time. We present PADA: Anexample-based autoregressive Prompt learning algorithm for on-the-flyAny-Domain Adaptation, based on the T5 language model. Given a test example,PADA first generates a unique prompt for it and then, conditioned on thisprompt, labels the example with respect to the NLP prediction task. PADA istrained to generate a prompt which is a token sequence of unrestricted length,consisting of Domain Related Features (DRFs) that characterize each of thesource domains. Intuitively, the generated prompt is a unique signature thatmaps the test example to a semantic space spanned by the source domains. Inexperiments with 3 tasks (text classification and sequence tagging), for atotal of 14 multi-source adaptation scenarios, PADA substantially outperformsstrong baselines.

PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains