Results 11 to 20 of about 3,637,202 (294)
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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Matthew effects in young readers : reading comprehension and reading experience aid vocabulary development [PDF]
, 2011 The authors report data from a longitudinal study of the reading development of children who were assessed in the years of their 8th, 11th, 14th, and 16th birthdays.
Oakhill, Jane, Cain, Kate
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Investigating the causes of reading comprehension failure: the comprehension-age match design. [PDF]
, 2000 The reading-level (or reading-age) match design has become a widely-used tool for investigating the possible direction of the relation between particular skills and word reading ability: Cause or consequence.
Cain, Kate +2 more
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Learn a prior question-aware feature for machine reading comprehension
Frontiers in Physics, 2022 Machine reading comprehension aims to train machines to comprehend a given context and then answer a series of questions according to their understanding of the context. It is the cornerstone of conversational reading comprehension and question answering
Yu Zhang, Bo Shen, Xing Cao
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Multi-Document Neural Reading Comprehension Based on Bi-Directional Attention Mechanism [PDF]
Jisuanji gongcheng, 2020 Machine Reading Comprehension(MRC) is a question and answer task that automatically generates or extracts corresponding answers for a given text and specific questions.This task is of great significance to evaluating the understanding of computer systems
TANG Hongxuan, WU Kaili, ZHU Mengmeng, HONG Yu
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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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