Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts

Luu, Son T.; Bui, Mao Nguyen; Nguyen, Loi Duc; Tran, Khiem Vinh; Nguyen, Kiet Van; Nguyen, Ngan Luu-Thuy

doi:10.1007/978-3-030-88113-9_44

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Besides, two Vietnamese QA systems [26,27] were developed with automatic reading comprehension techniques. In addition, ViMMRC [28] and ViCoQA [29] are two Vietnamese corpora for multiple-choices reading comprehension and conversational reading comprehension, respectively. Besides, a few MRC and QA methods have been studied on Vietnamese MRC datasets, such as BERT [23], ViReader [15], XLMRQA [26], and ViQAS [27].…”

Section: Background and Related Workmentioning

confidence: 99%

VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension

Nguyen

Tran²,

Nguyễn

et al. 2022

JCSCE

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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. While many datasets have been developed for MRC research for other languages, there is a lack of such resources for the Vietnamese language. Although many datasets and methodologies have been developed for English and Chinese, many Vietnamese machine reading comprehension limitations need to be solved further. 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 shared task on Vietnamese machine reading comprehension (VLSP2021-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. Each participant was provided with the training data, including 28,457 annotated question-answer pairs, and returned the result on a public test set of more than 3,821 questions and a private test set of 3,712 questions. In this article, we present details of the organization of the shared task, an overview of the methods employed by shared-task participants, and the results. The highest performances in this competition are 77.24% (in EM) and 67.43% (in F1-score) 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 that has achieved state-of-the-art results on many natural language processing tasks. We believe that releasing the UIT-ViQuAD 2.0 dataset motivates more researchers to improve Vietnamese machine reading comprehension.

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Section: Background and Related Workmentioning

confidence: 99%

VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension

Nguyen

Tran²,

Nguyễn

et al. 2022

JCSCE

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“…MRC is also starting to gain attention in Vietnam by releasing the specific datasets in Vietnamese such as ViQuAD1.0 [2], ViMMRC [19], UIT-ViNewsQA [20]. In addition, many research articles related to machine learning understanding Vietnamese have been published, some typical works like [21,22,23,24] showing the development of Vietnamese MRC. Later, with higher model readability requirements, later versions of the datasets added unanswered questions.…”

Section: Related Workmentioning

confidence: 99%

ViMRC - VLSP 2021: Context-Aware Answer Extraction in Vietnamese Question Answering

2022

JCSCE

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MRC is challenging the natural language processing fields; machines automatically have to answer questions based on specific passages for this task. In recent years, machine reading comprehension (MRC) has received much attention; many articles have been written about this task. However, most of those articles only develop models in two main languages, English and Chinese. In this article, we propose to apply a new model to the task of reading comprehension in Vietnamese. Specifically, we use BLANC (BLock AttentioN for Context prediction) on pre-trained baseline models to solve the Machine reading comprehension (MRC) task on Vietnamese. We have achieved good results when using BLANC on the baseline model. Specifically, with the MRC task at the VLSP share-task 2021, we scored 76.877% of F1-score on the private test and ranked 2nd in the total. This shows that BLANC works very well in MRC tasks and further enhances the Vietnamese MRC development.

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Numerical reasoning reading comprehension on Vietnamese COVID-19 news: task, corpus, and challenges

Van Nguyen,

Le,

2024

Neural Comput & Applic

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Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts

Cited by 3 publications

References 18 publications

VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension

VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension

ViMRC - VLSP 2021: Context-Aware Answer Extraction in Vietnamese Question Answering

Numerical reasoning reading comprehension on Vietnamese COVID-19 news: task, corpus, and challenges

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