TADA: Task-Agnostic Dialect Adapters for English

Understand

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.