# ViMMRC 2.0 -- Enhancing Machine Reading Comprehension on Vietnamese Literature Text

arXiv: https://arxiv.org/abs/2303.18162 · HTML: https://mlsanity.com/paper/2303.18162/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 45 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension](https://mlsanity.com/paper/1902.00164/) (2019)
- [Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension](https://mlsanity.com/paper/1904.09679/) (2019)
- [ParsiNLU: A Suite of Language Understanding Challenges for Persian](https://mlsanity.com/paper/2012.06154/) (2020)
- [A Vietnamese Dataset for Evaluating Machine Reading Comprehension](https://mlsanity.com/paper/2009.14725/) (2020)
- [Large-scale Cloze Test Dataset Created by Teachers](https://mlsanity.com/paper/1711.03225/) (2017)
- [English Machine Reading Comprehension Datasets: A Survey](https://mlsanity.com/paper/2101.10421/) (2021)
- [A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP](https://mlsanity.com/paper/2104.10810/) (2021)
- [New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles](https://mlsanity.com/paper/2006.11138/) (2020)
- [Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models](https://mlsanity.com/paper/2006.15994/) (2020)
- [MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension](https://mlsanity.com/paper/1910.00458/) (2019)
- [PQuAD: A Persian Question Answering Dataset](https://mlsanity.com/paper/2202.06219/) (2022)
- [Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian](https://mlsanity.com/paper/1908.01519/) (2019)
- [Sentence Extraction-Based Machine Reading Comprehension for Vietnamese](https://mlsanity.com/paper/2105.09043/) (2021)
- [Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts](https://mlsanity.com/paper/2105.01542/) (2021)
- [VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2203.11400/) (2022)
- [SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese](https://mlsanity.com/paper/2209.10482/) (2022)
- [Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization](https://mlsanity.com/paper/2108.13741/) (2021)
- [SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts](https://mlsanity.com/paper/2206.09600/) (2022)
- [An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension](https://mlsanity.com/paper/2008.08810/) (2020)
- [NLPBK at VLSP-2020 shared task: Compose transformer pretrained models for Reliable Intelligence Identification on Social network](https://mlsanity.com/paper/2101.12672/) (2021)

## Built on by (most cited first, 5 of 5)

- [Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education](https://mlsanity.com/paper/2310.12059/) (2023)
- [Generative Pre-trained Transformer for Vietnamese Community-based COVID-19 Question Answering](https://mlsanity.com/paper/2310.14602/) (2023)
- [VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension](https://mlsanity.com/paper/2402.02655/) (2024)
- [ViMultiChoice: Toward a Method That Gives Explanation for Multiple-Choice Reading Comprehension in Vietnamese](https://mlsanity.com/paper/2602.09961/) (2026)
- [ViCLSR: A Supervised Contrastive Learning Framework with Natural Language Inference for Natural Language Understanding Tasks](https://mlsanity.com/paper/2603.21084/) (2026)

## Abstract (arXiv)

Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose a multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. From the results of the error analysis, we found that the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research to enhance the ability of computers to understand the Vietnamese language.