A framework with TOPPing source selection and VACAI-Bowl dual-branch model yields 54.62% average improvement in dependency parsing across 10 low-resource varieties.
Pierrehumbert, and Furu Wei
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Japanese SLM dialect robustness correlates with base LLM robustness; both dialectal training data and speech-encoder fine-tuning raise SLM robustness.
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Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varieties
A framework with TOPPing source selection and VACAI-Bowl dual-branch model yields 54.62% average improvement in dependency parsing across 10 low-resource varieties.
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Evaluating Japanese Dialect Robustness Across Speech and Text-based Large Language Models
Japanese SLM dialect robustness correlates with base LLM robustness; both dialectal training data and speech-encoder fine-tuning raise SLM robustness.