Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts

• URL: http://arxiv.org/abs/2105.01542v2
• Date: Wed, 5 May 2021 01:48:26 GMT
• Title: Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts
• Authors: Son T. Luu, Mao Nguyen Bui, Loi Duc Nguyen, Khiem Vinh Tran, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
• Abstract summary: We present a new Vietnamese corpus for conversational machine reading comprehension (UIT-ViCoQA) UIT-ViCoQA consists of 10,000 questions with answers over 2,000 conversations about health news articles. The best model obtains an F1 score of 45.27%, which is 30.91 points behind human performance (76.18%), indicating that there is ample room for improvement.
• Score: 0.2446672595462589
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine reading comprehension (MRC) is a sub-field in natural language processing or computational linguistics. MRC aims to help computers understand unstructured texts and then answer questions related to them. In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension (UIT-ViCoQA), consisting of 10,000 questions with answers over 2,000 conversations about health news articles. We analyze UIT-ViCoQA in depth with different linguistic aspects. Then, we evaluate several baseline models about dialogue and reading comprehension on the UIT-ViCoQA corpus. The best model obtains an F1 score of 45.27%, which is 30.91 points behind human performance (76.18%), indicating that there is ample room for improvement.

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