Online data collection allows for access to diverse populations. In the current study, we used online recruitment and data collection methods to obtain a corpus of read speech from adult talkers representing three authentic regional dialects of American English and one novel dialect created for the corpus. The authentic dialects (New England, Northern, and Southern American English) are each represented by 8–10 talkers, ranging in age from 22 to 75 years old. The novel dialect was produced by five Spanish-English bilinguals with training in linguistics, who were asked to produce Spanish /o/ in an otherwise English segmental context. One vowel contrast was selected for each dialect, in which the vowels within the contrast are acoustically more similar in the target dialect than in the other dialects. Each talker produced one familiar short story with 40 tokens of each vowel within the target contrast for their dialect, as well as a set of real words and nonwords that represent both the target vowel contrast for their dialect and the other three vowel contrasts for comparison across dialects. Preliminary acoustic analysis reveals both cross-dialect and within-dialect variability in the target vowel contrasts. The corpus materials are available to the scholarly community.
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DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules
Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialects. While existing mitigations tackle discrepancies for individual target dialects, they assume access to high-accuracy dialect identification systems. The boundaries between dialects are inherently flexible, making it difficult to categorize language into discrete predefined categories. In this paper, we propose DADA (Dialect Adaptation via Dynamic Aggregation), a modular approach to imbue SAE-trained models with multi-dialectal robustness by composing adapters which handle specific linguistic features. The compositional architecture of DADA allows for both targeted adaptation to specific dialect variants and simultaneous adaptation to various dialects. We show that DADA is effective for both single task and instruction finetuned language models, offering an extensible and interpretable framework for adapting existing LLMs to different English dialects.
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- Award ID(s):
- 2247357
- PAR ID:
- 10506663
- Publisher / Repository:
- Association for Computational Linguistics
- Date Published:
- Journal Name:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Page Range / eLocation ID:
- 13776 to 13793
- Format(s):
- Medium: X
- Location:
- Singapore
- Sponsoring Org:
- National Science Foundation
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