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 Prompt learning algorithm for on-the-fly
Any-Domain Adaptation, based on the T5 language model. Given a test example,
PADA first generates a unique prompt for it and then, conditioned on this
prompt, labels the example with respect to the NLP prediction task. PADA is
trained to generate a prompt which is a token sequence of unrestricted length,
consisting of Domain Related Features (DRFs) that characterize each of the
source domains. Intuitively, the generated prompt is a unique signature that
maps the test example to a semantic space spanned by the source domains. In
experiments with 3 tasks (text classification and sequence tagging), for a
total of 14 multi-source adaptation scenarios, PADA substantially outperforms
strong baselines.

我们提出了一个基于自动回归 Prompt 学习算法的 PADA，用于实时任何领域自适应，其中生成的唯一提示作为源域特征的连续令牌序列，PADA 在多个任务中显着优于强基准。