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Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts

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arxiv 2105.01542 v6 pith:5GHLFTQW submitted 2021-05-04 cs.CL

Son T. Luu , Mao Nguyen Bui , Loi Duc Nguyen , Khiem Vinh Tran , Kiet Van Nguyen , Ngan Luu-Thuy Nguyen This is my paper · ORCID
classification cs.CL
keywords comprehensionmachineconversationalcorpusreadingtextsconversationlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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Machine reading comprehension (MRC) is a sub-field in natural language processing that aims to assist computers understand unstructured texts and then answer questions related to them. In practice, the conversation is an essential way to communicate and transfer information. To help machines understand conversation texts, we present UIT-ViCoQA, a new corpus for conversational machine reading comprehension in the Vietnamese language. This corpus consists of 10,000 questions with answers over 2,000 conversations about health news articles. Then, we evaluate several baseline approaches for conversational machine 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. Our dataset is available at our website: http://nlp.uit.edu.vn/datasets/ for research purposes.

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  1. A Vietnamese Dataset for Text Segmentation and Multiple Choices Reading Comprehension

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A new Vietnamese dataset for text segmentation and multiple-choice reading comprehension, with benchmarks showing multilingual BERT models lead on both tasks.

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