A Vietnamese Dataset for Text Segmentation and Multiple Choices Reading Comprehension

TL;DR

This paper introduces VSMRC, a new Vietnamese dataset for text segmentation and reading comprehension, demonstrating that multilingual models like mBERT outperform monolingual models on these tasks.

Contribution

The paper provides the first large-scale Vietnamese dataset for text segmentation and MRC, along with experimental evidence of the effectiveness of multilingual models.

Findings

mBERT achieves 88.01% accuracy on MRC

F1 score of 63.15% on text segmentation

Multilingual models outperform monolingual models for Vietnamese tasks

Abstract

Vietnamese, the 20th most spoken language with over 102 million native speakers, lacks robust resources for key natural language processing tasks such as text segmentation and machine reading comprehension (MRC). To address this gap, we present VSMRC, the Vietnamese Text Segmentation and Multiple-Choice Reading Comprehension Dataset. Sourced from Vietnamese Wikipedia, our dataset includes 15,942 documents for text segmentation and 16,347 synthetic multiple-choice question-answer pairs generated with human quality assurance, ensuring a reliable and diverse resource. Experiments show that mBERT consistently outperforms monolingual models on both tasks, achieving an accuracy of 88.01% on MRC test set and an F1 score of 63.15\% on text segmentation test set. Our analysis reveals that multilingual models excel in NLP tasks for Vietnamese, suggesting potential applications to other under-resourced languages. VSMRC is available at HuggingFace