  Natural Language Processing algorithms have made incredible progress
recently, but they still struggle when applied to out-of-distribution examples.
In this paper, we address a very challenging and previously underexplored
version of this domain adaptation problem. In our setup an algorithm is trained
on several source domains, and then applied to examples from an unseen domain
that is 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: A Prompt-based Autoregressive
Domain Adaptation algorithm, based on the T5 model. Given a test example, PADA
first generates a unique prompt and then, conditioned on this prompt, labels
the example with respect to the NLP task. The prompt is a sequence of
unrestricted length, consisting of pre-defined Domain Related Features (DRFs)
that characterize each of the source domains. Intuitively, the prompt is a
unique signature that maps the test example to the semantic space spanned by
the source domains. In experiments with two tasks: Rumour Detection and
Multi-Genre Natural Language Inference (MNLI), for a total of 10 multi-source
adaptation scenarios, PADA strongly outperforms state-of-the-art approaches and
additional strong baselines.
