Results 11 to 20 of about 335 (211)
Enhancing Machine Reading Comprehension With Position Information [PDF]
IEEE Access, 2019 When people do the reading comprehension, they often try to find the words from the passages which are similar to the question words first. Then people deduce the answer based on the context around these similar words. Therefore, the position information
Yajing Xu +6 more
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Coreference Reasoning in Machine Reading Comprehension [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 Coreference resolution is essential for natural language understanding and has been long studied in NLP. In recent years, as the format of Question Answering (QA) became a standard for machine reading comprehension (MRC), there have been data collection efforts, e.g., Dasigi et al.
Wu, M. +3 more
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A Survey on Machine Reading Comprehension Systems [PDF]
Natural Language Engineering, 2022 AbstractMachine Reading Comprehension (MRC) is a challenging task and hot topic in Natural Language Processing. The goal of this field is to develop systems for answering the questions regarding a given context. In this paper, we present a comprehensive survey on diverse aspects of MRC systems, including their approaches, structures, input/outputs, and
Razieh Baradaran +2 more
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Review of Conversational Machine Reading Comprehension
Jisuanji kexue yu tansuo, 2021 Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text.
LI Kun, LI Yanling, LIN Min
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RoBERTa-CoA: RoBERTa-Based Effective Finetuning Method Using Co-Attention
IEEE Access, 2023 In the field of natural language processing, artificial intelligence (AI) technology has been utilized to solve various problems, such as text classification, similarity measurement, chatbots, machine translation, and machine reading comprehension ...
Jeong-Hoon Kim +5 more
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Event Extraction as Machine Reading Comprehension [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Previous methods for EE typically model it as a classification task, which are usually prone to the data scarcity problem. In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension ...
Jian Liu 0032 +4 more
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English Machine Reading Comprehension Datasets: A Survey [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 This paper surveys 60 English Machine Reading Comprehension datasets, with a view to providing a convenient resource for other researchers interested in this problem. We categorize the datasets according to their question and answer form and compare them across various dimensions including size, vocabulary, data source, method of creation, human ...
Dzendzik, Daria +2 more
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Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
Transactions of the Association for Computational Linguistics, 2020 Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset ...
Sun, Kai +3 more
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Event Extraction Method Based on Conversational Machine Reading Comprehension Model [PDF]
Jisuanji kexue, 2023 Event extraction aims to extract structured information automatically from massive unstructured texts to help people quickly understand the latest developments of events.Traditional methods are mainly implemented by classification or sequence labeling ...
LIU Luping, ZHOU Xin, CHEN Junjun, He Xiaohai, QING Linbo, WANG Meiling
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RoR: Read-over-Read for Long Document Machine Reading Comprehension [PDF]
Findings of the Association for Computational Linguistics: EMNLP 2021, 2021 Accepted as findings of ...
Jing Zhao +6 more
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