Dialect Normalization using Large Language Models and Morphological Rules

ACL ARR 2025 February Submission4789 Authors

Abstract:

Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle this issue by transforming dialectal text so that it can be used by standard-language tools downstream. In this study, we tackle this task by introducing a new normalization method that combines rule-based linguistically informed transformations and large language models (LLMs) with targeted few-shot prompting, without requiring any parallel data. We implement our method for Greek dialects and apply it on a dataset of regional proverbs, evaluating the outputs using human annotators.
We then use this dataset to conduct downstream experiments, finding that previous results regarding these proverbs relied solely on superficial linguistic information, including orthographic artifacts, while new observations can still be made through the remaining semantics.

Paper Type: Short

Research Area: Multilingualism and Cross-Lingual NLP

Research Area Keywords: dialects and language varieties, linguistic variation, cross-lingual transfer, less-resourced languages, software and tools

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Publicly available software and/or pre-trained models, Data analysis

Languages Studied: Standard Modern Greek, Pontic, Cypriot, Cretan, Anatolian Greek, Lesbian, Macedonian Greek, Thracian, Epirote, Heptanesian, Peloponnesian, Cycladic, Dodecanesian

Submission Number: 4789