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XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source XLMRQA:基于越南语维基百科文本知识源的开放领域问答
Pub Date : 2022-04-14 DOI: 10.48550/arXiv.2204.07002
Kiet Van Nguyen, Phong Nguyen-Thuan Do, N. D. Nguyen, T. Huynh, A. Nguyen, N. Nguyen
. Question answering (QA) is a natural language understanding task within the fields of information retrieval and information extraction that has attracted much attention from the computational linguistics and artificial intelligence research community in recent years because of the strong development of machine reading comprehension-based models. A reader-based QA system is a high-level search engine that can find correct answers to queries or questions in open-domain or domain-specific texts using machine reading comprehension (MRC) techniques. The majority of advancements in data resources and machine-learning approaches in the MRC and QA systems, on the other hand, especially in two resource-rich languages such as English and Chinese. A low-resource language like Vietnamese has witnessed a scarcity of research on QA systems. This paper presents XLMRQA, the first Vietnamese QA system using a supervised transformer-based reader on the Wikipedia-based textual knowledge source (using the UIT-ViQuAD corpus), outperforming the two robust QA systems using deep neural network models: DrQA and BERTserini with 24.46% and 6.28%, respectively. From the results obtained on the three systems, we analyze the influence of question types on the performance of the QA systems.
. 问答(Question answer, QA)是信息检索和信息提取领域的一种自然语言理解任务,近年来由于基于机器阅读理解模型的蓬勃发展而受到计算语言学和人工智能研究界的广泛关注。基于读者的QA系统是一个高级搜索引擎,它可以使用机器阅读理解(MRC)技术在开放领域或特定领域的文本中找到查询或问题的正确答案。另一方面,MRC和QA系统中数据资源和机器学习方法的大部分进步,特别是在两种资源丰富的语言(如英语和中文)中。像越南语这样资源匮乏的语言,对QA系统的研究很少。本文提出了XLMRQA,这是越南第一个基于维基百科文本知识库(使用unit - viquad语料库)的基于监督变压器的阅读器的QA系统,其性能分别优于使用深度神经网络模型的两个鲁棒QA系统:DrQA和BERTserini,分别为24.46%和6.28%。根据在三个系统上得到的结果,我们分析了问题类型对QA系统性能的影响。
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引用次数: 3
Assembly Process Modeling Through Long Short-Term Memory 基于长短期记忆的装配过程建模
Pub Date : 2021-04-07 DOI: 10.1007/978-981-16-1685-3_3
Stefan-Alexandru Precup, A. Gellert, Alexandru Dorobanţiu, C. Zamfirescu
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引用次数: 6
NovemE - Color Space Net for Image Classification 用于图像分类的色彩空间网
Pub Date : 2021-04-07 DOI: 10.1007/978-3-030-73280-6_42
Urvi Oza, Sarangi Patel, Pankaj Kumar
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引用次数: 1
Trident: Change Point Detection for Multivariate Time Series via Dual-Level Attention Learning 三叉戟:基于双水平注意学习的多元时间序列变化点检测
Pub Date : 2021-04-07 DOI: 10.1007/978-3-030-73280-6_63
Ziyi Duan, Haizhou Du, Yang Zheng
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引用次数: 0
A General Modeling Framework for Robust Optimization 鲁棒优化的通用建模框架
Pub Date : 2021-04-07 DOI: 10.1007/978-981-16-1685-3_31
M. Drwal
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引用次数: 0
Intelligent Information and Database Systems: Recent Developments, 11th Asian Conference, ACIIDS 2019, Yogyakarta, Indonesia, 8-11 April 2019, Extended Posters 智能信息和数据库系统:最新发展,第11届亚洲会议,ACIIDS 2019,日惹,印度尼西亚,2019年4月8-11日,扩展海报
Pub Date : 2020-09-25 DOI: 10.1007/978-3-030-14132-5
Maciej Huk, Marcin Maleszka
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引用次数: 14
A kNN Based Position Prediction Method for SNS Places 基于kNN的SNS场所位置预测方法
Pub Date : 2020-03-23 DOI: 10.1007/978-3-030-42058-1_22
Jong-Shin Chen, Huai-Yi Huang, Chi-Yueh Hsu
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引用次数: 9
Design and Life Cycle Data Analysis for Smart Metering 智能电表的设计与生命周期数据分析
Pub Date : 2020-03-23 DOI: 10.1007/978-981-15-3380-8_25
J. Horalek, V. Sobeslav
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引用次数: 1
Safe and Secure Software-Defined Networks for Smart Electricity Substations 安全可靠的智能变电站软件定义网络
Pub Date : 2020-03-23 DOI: 10.1007/978-981-15-3380-8_16
Filip Holík, M. Broadbent, M. Findrik, Paul Smith, N. Race
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引用次数: 2
Stock Return Prediction Using Dual-Stage Attention Model with Stock Relation Inference 基于股票关系推理的双阶段注意模型的股票收益预测
Pub Date : 2020-03-23 DOI: 10.1007/978-3-030-41964-6_42
Tanawat Chiewhawan, P. Vateekul
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引用次数: 2
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Asian Conference on Intelligent Information and Database Systems
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