Results 21 to 30 of about 3,721,524 (172)
Exploring Machine Reading Comprehension for Continuous Questions via Subsequent Question Completion
IEEE Access, 2021 In recent years, the Sq-MRC (machine reading comprehension for separate questions) task, where the questioner poses a separate question each time, has experienced rapid development.
Kaijing Yang, Xin Zhang, Dongmei Chen
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IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 Machine Reading Comprehension (MRC) has become one of the essential tasks in Natural Language Understanding (NLU) as it is often included in several NLU benchmarks (Liang et al., 2020; Wilie et al., 2020). However, most MRC datasets only have answerable question type, overlooking the importance of unanswerable questions.
Rifki Afina Putri, Alice Oh
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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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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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NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension
CoRR, 2023 Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research focuses on utilizing massive extra data, including pre-training corpora and incorporating search engines.
Yuxiang Zhang +4 more
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Applied Sciences, 2022 Machine reading comprehension (MRC) is an important research topic in the field of Natural Language Processing (NLP). However, traditional MRC models often face challenges of information loss, lack of capability to retain long-distance dependence, and ...
Nawei Shi +2 more
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Neural Processing Letters, 2023 Financial named entity recognition (FinNER) from literature is a challenging task in the field of financial text information extraction, which aims to extract a large amount of financial knowledge from unstructured texts. It is widely accepted to use sequence tagging frameworks to implement FinNER tasks.
Yuzhe Zhang 0002, Hong Zhang
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Benchmarking Machine Reading Comprehension: A Psychological Perspective [PDF]
, 2021 Machine reading comprehension (MRC) has received considerable attention as a benchmark for natural language understanding. However, the conventional task design of MRC lacks explainability beyond the model interpretation, i.e., reading comprehension by a
Aizawa, Akiko +2 more
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Machine Reading Comprehension Framework Based on Self-Training for Domain Adaptation
IEEE Access, 2021 Machine reading comprehension (MRC) is a type of question answering mechanism in which a computer reads documents and answers related questions. The accuracies of recent MRC systems surpass those of humans.
Hyeon-Gu Lee, Youngjin Jang, Harksoo Kim
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Machine Reading Comprehension: Challenges and Approaches [PDF]
, 2021 142 pagesMachine reading comprehension (MRC) tasks have attracted substantial attention from both academia and industry. These tasks require a machine reader to answer questions relevant to a given document provided as input.
Sun, Kai
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