Abstract
Text normalization methods have been commonly applied to historical language or user-generated content, but less often to dialectal transcriptions. In this paper, we introduce dialect-to-standard normalization – i.e., mapping phonetic transcriptions from different dialects to the orthographic norm of the standard variety – as a distinct sentence-level character transduction task and provide a large-scale analysis of dialect-to-standard normalization methods. To this end, we compile a multilingual dataset covering four languages: Finnish, Norwegian, Swiss German and Slovene. For the two biggest corpora, we provide three different data splits corresponding to different use cases for automatic normalization. We evaluate the most successful sequence-to-sequence model architectures proposed for text normalization tasks using different tokenization approaches and context sizes. We find that a character-level Transformer trained on sliding windows of three words works best for Finnish, Swiss German and Slovene, whereas the pre-trained byT5 model using full sentences obtains the best results for Norwegian. Finally, we perform an error analysis to evaluate the effect of different data splits on model performance.- Anthology ID:
- 2023.findings-emnlp.923
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13814–13828
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.923
- DOI:
- 10.18653/v1/2023.findings-emnlp.923
- Cite (ACL):
- Olli Kuparinen, Aleksandra Miletić, and Yves Scherrer. 2023. Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13814–13828, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation (Kuparinen et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/aacl-23-doi-ingestion/2023.findings-emnlp.923.pdf