  Large Language Models, the dominant starting point for Natural Language
Processing (NLP) applications, fail at a higher rate for speakers of English
dialects other than Standard American English (SAE). Prior work addresses this
using task-specific data or synthetic data augmentation, both of which require
intervention for each dialect and task pair. This poses a scalability issue
that prevents the broad adoption of robust dialectal English NLP. We introduce
a simple yet effective method for task-agnostic dialect adaptation by aligning
non-SAE dialects using adapters and composing them with task-specific adapters
from SAE. Task-Agnostic Dialect Adapters (TADA) improve dialectal robustness on
4 dialectal variants of the GLUE benchmark without task-specific supervision.
