A Vietnamese Dataset for Text Segmentation and Multiple Choices Reading Comprehension

• URL: http://arxiv.org/abs/2506.15978v1
• Date: Thu, 19 Jun 2025 02:53:24 GMT
• Title: A Vietnamese Dataset for Text Segmentation and Multiple Choices Reading Comprehension
• Authors: Toan Nguyen Hai, Ha Nguyen Viet, Truong Quan Xuan, Duc Do Minh,
• Abstract summary: Vietnamese is the 20th most spoken language with over 102 million native speakers.<n>Our dataset includes 15,942 documents for text segmentation and 16,347 synthetic multiple-choice question-answer pairs.<n>Experiments show that mBERT consistently outperforms monolingual models on both tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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

Related papers

• VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension [1.3942150186842373]
  This paper presents the development process of a Vietnamese spoken language corpus for machine reading comprehension tasks. The existing MRC corpora in Vietnamese mainly focus on formal written documents such as Wikipedia articles, online newspapers, or textbooks. In contrast, the VlogQA consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube.
  arXiv  Detail & Related papers  (2024-02-05T00:54:40Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• MegaWika: Millions of reports and their sources across 50 diverse languages [74.3909725023673]
  MegaWika consists of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials. We process this dataset for a myriad of applications, including translating non-English articles for cross-lingual applications. MegaWika is the largest resource for sentence-level report generation and the only report generation dataset that is multilingual.
  arXiv  Detail & Related papers  (2023-07-13T20:04:02Z)
• Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining [65.30528567491984]
  This paper proposes a method for zero-shot multilingual TTS using text-only data for the target language. The use of text-only data allows the development of TTS systems for low-resource languages. Evaluation results demonstrate highly intelligible zero-shot TTS with a character error rate of less than 12% for an unseen language.
  arXiv  Detail & Related papers  (2023-01-30T00:53:50Z)
• Sentence Extraction-Based Machine Reading Comprehension for Vietnamese [0.2446672595462589]
  We introduce the UIT-ViWikiQA, the first dataset for evaluating sentence extraction-based machine reading comprehension in Vietnamese language. The dataset consists of comprises 23.074 question-answers based on 5.109 passages of 174 Vietnamese articles from Wikipedia. Our experiments show that the best machine model is XLM-R$_Large, which achieves an exact match (EM) score of 85.97% and an F1-score of 88.77% on our dataset.
  arXiv  Detail & Related papers  (2021-05-19T10:22:27Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• A Vietnamese Dataset for Evaluating Machine Reading Comprehension [2.7528170226206443]
  We present UIT-ViQuAD, a new dataset for the low-resource language as Vietnamese to evaluate machine reading comprehension models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. We conduct experiments on state-of-the-art MRC methods for English and Chinese as the first experimental models on UIT-ViQuAD.
  arXiv  Detail & Related papers  (2020-09-30T15:06:56Z)
• An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension [2.7528170226206443]
  We conduct experiments on neural network-based model to understand the impact of word representation to machine reading comprehension. Our experiments include using the Co-match model on six different Vietnamese word embeddings and the BERT model for multiple-choice reading comprehension. On the ViMMRC corpus, the accuracy of BERT model is 61.28% on test set.
  arXiv  Detail & Related papers  (2020-08-20T07:29:14Z)
• A High-Quality Multilingual Dataset for Structured Documentation Translation [101.41835967142521]
  This paper presents a high-quality multilingual dataset for the documentation domain. We collect XML-structured parallel text segments from the online documentation for an enterprise software platform.
  arXiv  Detail & Related papers  (2020-06-24T02:08:44Z)
• A Sentence Cloze Dataset for Chinese Machine Reading Comprehension [64.07894249743767]
  We propose a new task called Sentence Cloze-style Machine Reading (SC-MRC) The proposed task aims to fill the right candidate sentence into the passage that has several blanks. We built a Chinese dataset called CMRC 2019 to evaluate the difficulty of the SC-MRC task.
  arXiv  Detail & Related papers  (2020-04-07T04:09:00Z)
• Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension [2.5199066832791535]
  We construct a dataset which consists of 2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese texts. We propose a lexical-based MRC method that utilizes semantic similarity measures and external knowledge sources to analyze questions and extract answers from the given text. Our proposed method achieves 61.81% by accuracy, which is 5.51% higher than the best baseline model.
  arXiv  Detail & Related papers  (2020-01-16T08:09:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.