Academic

ViDia2Std: A Parallel Corpus and Methods for Low-Resource Vietnamese Dialect-to-Standard Translation

arXiv:2603.10211v1 Announce Type: new Abstract: Vietnamese exhibits extensive dialectal variation, posing challenges for NLP systems trained predominantly on standard Vietnamese. Such systems often underperform on dialectal inputs, especially from underrepresented Central and Southern regions. Previous work on dialect normalization has focused narrowly on Central-to-Northern dialect transfer using synthetic data and limited dialectal diversity. These efforts exclude Southern varieties and intra-regional variants within the North. We introduce ViDia2Std, the first manually annotated parallel corpus for dialect-to-standard Vietnamese translation covering all 63 provinces. Unlike prior datasets, ViDia2Std includes diverse dialects from Central, Southern, and non-standard Northern regions often absent from existing resources, making it the most dialectally inclusive corpus to date. The dataset consists of over 13,000 sentence pairs sourced from real-world Facebook comments and annotated b

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Khoa Anh Ta, Nguyen Van Dinh, Kiet Van Nguyen · · 1 min read · 14 views
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arXiv:2603.10211v1 Announce Type: new Abstract: Vietnamese exhibits extensive dialectal variation, posing challenges for NLP systems trained predominantly on standard Vietnamese. Such systems often underperform on dialectal inputs, especially from underrepresented Central and Southern regions. Previous work on dialect normalization has focused narrowly on Central-to-Northern dialect transfer using synthetic data and limited dialectal diversity. These efforts exclude Southern varieties and intra-regional variants within the North. We introduce ViDia2Std, the first manually annotated parallel corpus for dialect-to-standard Vietnamese translation covering all 63 provinces. Unlike prior datasets, ViDia2Std includes diverse dialects from Central, Southern, and non-standard Northern regions often absent from existing resources, making it the most dialectally inclusive corpus to date. The dataset consists of over 13,000 sentence pairs sourced from real-world Facebook comments and annotated by native speakers across all three dialect regions. To assess annotation consistency, we define a semantic mapping agreement metric that accounts for synonymous standard mappings across annotators. Based on this criterion, we report agreement rates of 86% (North), 82% (Central), and 85% (South). We benchmark several sequence-to-sequence models on ViDia2Std. mBART-large-50 achieves the best results (BLEU 0.8166, ROUGE-L 0.9384, METEOR 0.8925), while ViT5-base offers competitive performance with fewer parameters. ViDia2Std demonstrates that dialect normalization substantially improves downstream tasks, highlighting the need for dialect-aware resources in building robust Vietnamese NLP systems.

Executive Summary

This article presents ViDia2Std, a novel parallel corpus and methods for low-resource Vietnamese dialect-to-standard translation. The corpus comprises over 13,000 sentence pairs sourced from real-world Facebook comments and annotated by native speakers across three dialect regions. The authors demonstrate that ViDia2Std, covering all 63 provinces, is the most dialectally inclusive corpus to date. They also benchmark several sequence-to-sequence models on ViDia2Std, showing that dialect normalization substantially improves downstream tasks. The results highlight the need for dialect-aware resources in building robust Vietnamese NLP systems. The study's findings have significant implications for the development of more accurate and inclusive language processing models.

Key Points

  • ▸ ViDia2Std is the first manually annotated parallel corpus for dialect-to-standard Vietnamese translation covering all 63 provinces.
  • ▸ The corpus includes diverse dialects from Central, Southern, and non-standard Northern regions often absent from existing resources.
  • ▸ Sequence-to-sequence models, particularly mBART-large-50, achieve state-of-the-art results on ViDia2Std, demonstrating the importance of dialect normalization.

Merits

Strength in Dialect Coverage

ViDia2Std encompasses a vast range of Vietnamese dialects, making it a valuable resource for NLP systems aiming to address dialectal variation.

Methodological Innovation

The study introduces a novel approach to dialect normalization, leveraging real-world data and annotator agreement metrics to ensure the quality of the corpus.

Demerits

Limited Annotation Consistency

The study reports varying agreement rates among annotators from different dialect regions, which may impact the reliability of the corpus.

Dependence on Real-World Data

ViDia2Std's reliance on Facebook comments may limit its generalizability to other domains or applications.

Expert Commentary

The article's contributions to the field of NLP are substantial, particularly in addressing the challenges of dialectal variation in Vietnamese. However, the study's limitations, such as the reliance on real-world data and varying annotation consistency, necessitate further investigation. The use of sequence-to-sequence models and dialect normalization demonstrates the potential of these approaches in improving the accuracy and inclusivity of language processing models. Future research should build upon these findings to create more robust and dialect-aware NLP systems.

Recommendations

  • ✓ Develop more robust and dialect-aware NLP systems that can accurately process and generate text in diverse dialects.
  • ✓ Create additional dialectally inclusive corpora for Vietnamese and other languages to further address the challenges of dialectal variation.

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