arXiv Open Access 2023

TADA: Task-Agnostic Dialect Adapters for English

Will Held Caleb Ziems Diyi Yang

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Abstrak

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.

Topik & Kata Kunci

cs.CL

Penulis (3)

Will Held

Caleb Ziems

Diyi Yang

Format Sitasi

APA MLA BibTeX

Held, W., Ziems, C., Yang, D. (2023). TADA: Task-Agnostic Dialect Adapters for English. https://arxiv.org/abs/2305.16651

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓