Results 21 to 30 of about 8,093 (292)

Machine translation output assessment and its impact on reading comprehension [PDF]

Fanāvarī-i āmūzish, 2020
Background and Objectives: Machine translation is now widely used everywhere; However, its role as a language learning tool has not been confirmed, as there are concerns about its quality.
V.R. Mirzaeian
doaj   +2 more sources

A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning

Applied Sciences, 2021
To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multi-granularity semantic reasoning.
Yu Dai, Yufan Fu, Lei Yang
doaj   +2 more sources

Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension

Applied Sciences, 2022
In recent years, pre-trained language models, represented by the bidirectional encoder representations from transformers (BERT) model, have achieved remarkable success in machine reading comprehension (MRC).
Chuanyun Xu, Zixu Liu, Gang Li, Changpeng Zhu, Yang Zhang   +4 more
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Machine Reading Comprehension Model Based on Fusion of Mixed Attention

Applied Sciences
To address the problems of the insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model ...
Yanfeng Wang, Ning Ma, Zechen Guo
doaj   +2 more sources

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., Moosavi, N.S., Roth, D., Gurevych, I.   +3 more
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Building machine reading comprehension model from scratch [PDF]

, 2023
In this paper, we introduce a machine reading comprehension model and how we built this model from scratch. Reading comprehension is a crucial requisite for artificial intelligence applications, such as Question-Answering systems, chatbots, virtual ...
Yang, Zijian Győző, Ligeti-Nagy, Noémi   +1 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, Razieh Ghiasi, Hossein Amirkhani   +2 more
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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
doaj   +1 more source

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
doaj   +1 more source

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, Yubo Chen 0001, Kang Liu 0001, Wei Bi, Xiaojiang Liu   +4 more
openaire   +1 more source