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    <abstract>Over 97 million inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the task of understanding a document or text, and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands complicate reasoning such as single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods in English and Chinese as the first experimental models on UIT-ViQuAD, which will be compared to further models. We also estimate human performances on the dataset and compare it to the experimental results of several powerful machine models. As a result, the substantial differences between humans and the best model performances on the dataset indicate that improvements can be explored on UIT-ViQuAD through future research. Our dataset is freely available to encourage the research community to overcome challenges in Vietnamese MRC.</abstract>
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%0 Conference Proceedings
%T A Vietnamese Dataset for Evaluating Machine Reading Comprehension
%A Nguyen, Kiet
%A Nguyen, Vu
%A Nguyen, Anh
%A Nguyen, Ngan
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F nguyen-etal-2020-vietnamese
%X Over 97 million inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the task of understanding a document or text, and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands complicate reasoning such as single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods in English and Chinese as the first experimental models on UIT-ViQuAD, which will be compared to further models. We also estimate human performances on the dataset and compare it to the experimental results of several powerful machine models. As a result, the substantial differences between humans and the best model performances on the dataset indicate that improvements can be explored on UIT-ViQuAD through future research. Our dataset is freely available to encourage the research community to overcome challenges in Vietnamese MRC.
%R 10.18653/v1/2020.coling-main.233
%U https://aclanthology.org/2020.coling-main.233
%U https://doi.org/10.18653/v1/2020.coling-main.233
%P 2595-2605

Markdown (Informal)

[A Vietnamese Dataset for Evaluating Machine Reading Comprehension](https://aclanthology.org/2020.coling-main.233) (Nguyen et al., COLING 2020)

• A Vietnamese Dataset for Evaluating Machine Reading Comprehension (Nguyen et al., COLING 2020)

ACL

• Kiet Nguyen, Vu Nguyen, Anh Nguyen, and Ngan Nguyen. 2020. A Vietnamese Dataset for Evaluating Machine Reading Comprehension. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2595–2605, Barcelona, Spain (Online). International Committee on Computational Linguistics.