A Vietnamese Dataset for Text Segmentation and Multiple Choices Reading Comprehension
This provides a dataset for Vietnamese NLP, addressing a gap for an under-resourced language, but it is incremental as it applies existing methods to new data.
The authors tackled the lack of resources for Vietnamese NLP by creating VSMRC, a dataset for text segmentation and multiple-choice reading comprehension, achieving 88.01% accuracy on MRC and 63.15% F1 on segmentation with mBERT.
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