# VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension arXiv: https://arxiv.org/abs/2203.11400 ยท HTML: https://mlsanity.com/paper/2203.11400/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 42 references that are in the map. - First posted on arXiv 22 Mar 2022. ## Builds on (most cited first, 28 of 28) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020) - [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017) - [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018) - [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017) - [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017) - [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018) - [The NarrativeQA Reading Comprehension Challenge](https://mlsanity.com/paper/1712.07040/) (2017) - [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016) - [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016) - [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017) - [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension](https://mlsanity.com/paper/1910.09753/) (2019) - [Retrospective Reader for Machine Reading Comprehension](https://mlsanity.com/paper/2001.09694/) (2020) - [A Span-Extraction Dataset for Chinese Machine Reading Comprehension](https://mlsanity.com/paper/1810.07366/) (2018) - [What Makes Reading Comprehension Questions Easier?](https://mlsanity.com/paper/1808.09384/) (2018) - [FQuAD: French Question Answering Dataset](https://mlsanity.com/paper/2002.06071/) (2020) - [Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets](https://mlsanity.com/paper/1911.09241/) (2019) - [KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension](https://mlsanity.com/paper/1909.07005/) (2019) - [SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis](https://mlsanity.com/paper/1912.09723/) (2019) - [A Vietnamese Dataset for Evaluating Machine Reading Comprehension](https://mlsanity.com/paper/2009.14725/) (2020) - [New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles](https://mlsanity.com/paper/2006.11138/) (2020) - [Context-Aware Answer Extraction in Question Answering](https://mlsanity.com/paper/2011.02687/) (2020) - [Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts](https://mlsanity.com/paper/2105.01542/) (2021) - [FQuAD2.0: French Question Answering and knowing that you know nothing](https://mlsanity.com/paper/2109.13209/) (2021) - [An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension](https://mlsanity.com/paper/2008.08810/) (2020) - [SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning](https://mlsanity.com/paper/2105.14879/) (2021) ## Built on by (most cited first, 7 of 7) - [SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese](https://mlsanity.com/paper/2209.10482/) (2022) - [ViMMRC 2.0 -- Enhancing Machine Reading Comprehension on Vietnamese Literature Text](https://mlsanity.com/paper/2303.18162/) (2023) - [The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models](https://mlsanity.com/paper/2302.00094/) (2023) - [Single-Sentence Reader: A Novel Approach for Addressing Answer Position Bias](https://mlsanity.com/paper/2308.04566/) (2023) - [Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2301.00429/) (2023) - [Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering](https://mlsanity.com/paper/2303.12671/) (2023) - [AGent: A Novel Pipeline for Automatically Creating Unanswerable Questions](https://mlsanity.com/paper/2309.05103/) (2023) ## Abstract (arXiv) One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 77.24% in F1-score and 67.43% in Exact Match on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model based on the transformer architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further explore the Vietnamese machine reading comprehension task and related tasks such as question answering, question generation, and natural language inference.