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

Eyal Ben-David, Nadav Oved, Roi Reichart


Abstract
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 that 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.1
Anthology ID:
2022.tacl-1.24
Volume:
Transactions of the Association for Computational Linguistics, Volume 10
Month:
Year:
2022
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
414–433
Language:
URL:
https://aclanthology.org/2022.tacl-1.24
DOI:
10.1162/tacl_a_00468
Bibkey:
Cite (ACL):
Eyal Ben-David, Nadav Oved, and Roi Reichart. 2022. PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains. Transactions of the Association for Computational Linguistics, 10:414–433.
Cite (Informal):
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains (Ben-David et al., TACL 2022)
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PDF:
https://aclanthology.org/2022.tacl-1.24.pdf