Modeling Orthographic and Lexical Variation Across Occitan Dialects

Core Concepts

Large multilingual language models can effectively represent orthographic and lexical variation across Occitan dialects without the need for extensive data normalization.

Abstract

This study investigates the ability of a fine-tuned multilingual BERT (mBERT) model to represent the orthographic and lexical variation across four dialects of Occitan, a low-resource Western Romance language. The authors first compile a parallel lexicon covering four Occitan dialects (Lengadocian, Lemosin, Provençau, and Gascon) to enable controlled evaluation. They then fine-tune mBERT on a multi-dialect Occitan corpus and conduct a series of experiments: Intrinsic Evaluation: Analogy Computation: The fine-tuned model performs poorly on semantic analogies but better on syntactic analogies, suggesting limitations in representing semantic relations across dialects. Lengadocian Lexicon Induction: The fine-tuned model can more accurately induce the Lengadocian lexicon from other dialects when the words have high surface similarity, indicating that surface similarity is an important factor. Extrinsic Evaluation: Part-of-Speech Tagging: The fine-tuned model achieves high accuracy on PoS tagging, even when trained only on data from the Lengadocian dialect and tested on all four dialects. Universal Dependency Parsing: The fine-tuned model's performance on UD parsing is robust to dialectal variation, though it struggles more with the Provençau dialect. The results suggest that large multilingual language models can effectively represent orthographic and lexical variation across Occitan dialects without the need for extensive data normalization during pre-processing. However, the model still struggles to fully capture semantic relations between parallel lexical items with low surface similarity across dialects.

Stats

Occitan has six main dialects, with significant lexical and orthographic variation between them. The parallel lexicon compiled for this study contains over 2,200 entries across four Occitan dialects. The fine-tuning corpus contains 386,552 lines (10,941,124 tokens) of Occitan data from Wikipedia discussions and parallel corpora.

Quotes

"Effectively normalizing textual data poses a considerable challenge, especially for low-resource languages lacking standardized writing systems." "Our findings suggest that large multilingual models minimize the need for spelling normalization during pre-processing."

Key Insights Distilled From

Modeling Orthographic Variation in Occitan's Dialects

by Zach... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19315.pdf

Deeper Inquiries

How could the model's representation of semantic relations across Occitan dialects be improved, beyond just capturing surface similarity?

To enhance the model's representation of semantic relations across Occitan dialects, we could explore incorporating more sophisticated pre-training tasks that focus on capturing semantic nuances. For instance, utilizing tasks that require the model to understand and generate text with specific semantic relationships could help it develop a deeper understanding of the underlying meaning of words beyond just their surface forms. Additionally, incorporating knowledge graphs or semantic networks specific to Occitan dialects could provide the model with richer semantic information to draw upon during fine-tuning. By exposing the model to a wider range of semantic contexts and relationships during pre-training, it may develop a more robust understanding of semantic concepts across dialectal variations.

What other techniques, beyond fine-tuning, could be used to better leverage the multilingual nature of the pre-trained model for low-resource languages like Occitan?

In addition to fine-tuning, leveraging the multilingual nature of the pre-trained model for low-resource languages like Occitan can be enhanced through techniques such as cross-lingual transfer learning and data augmentation. Cross-lingual transfer learning involves training the model on data from related languages that share similarities with Occitan, allowing it to learn general language patterns that can be beneficial for low-resource languages. Data augmentation techniques, such as back-translation and synthetic data generation, can also be employed to increase the diversity and quantity of training data available for the model. By generating additional training examples through translation and other methods, the model can learn to generalize better across dialectal variations and improve its performance on downstream tasks.

How might the results of this study apply to other low-resource languages with significant dialectal variation, but lacking standardized orthography and resources?

The findings of this study can be extrapolated to other low-resource languages facing similar challenges of significant dialectal variation and lack of standardized orthography and resources. By demonstrating that including non-standardized data from multiple dialects in fine-tuning does not necessarily harm model performance, this study provides insights for researchers working on other low-resource languages. The approach of fine-tuning with multi-dialect data can be applied to languages with diverse dialects to improve the model's ability to handle variation and capture dialect-specific features. Additionally, the emphasis on surface similarity and the importance of training data quality and diversity can guide researchers in developing effective strategies for leveraging pre-trained models in the context of low-resource languages with dialectal variation.