Results 21 to 30 of about 335 (211)
BioMRC: A Dataset for Biomedical Machine Reading Comprehension [PDF]
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, 2020 10 pages, 4 figures, 5 ...
Dimitris Pappas +3 more
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UDDIPOK: A reading comprehension based question answering dataset in Bangla language
Data in Brief, 2023 The popularity of reading comprehension (RC) is increasing day-to-day in Bangla Natural Language Processing (NLP) research area, both in machine learning and deep learning techniques.
Tanjim Taharat Aurpa +4 more
doaj +1 more source
Cross-Lingual Machine Reading Comprehension [PDF]
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019 10 pages, accepted as a conference paper at EMNLP-IJCNLP 2019 (long paper)
Yiming Cui 0001 +5 more
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Retrospective Reader for Machine Reading Comprehension
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Machine reading comprehension (MRC) is an AI challenge that requires machines to determine the correct answers to questions based on a given passage. MRC systems must not only answer questions when necessary but also tactfully abstain from answering when no answer is available according to the given passage.
Zhuosheng Zhang 0001 +2 more
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Towards Confident Machine Reading Comprehension
CoRR, 2021 There has been considerable progress on academic benchmarks for the Reading Comprehension (RC) task with State-of-the-Art models closing the gap with human performance on extractive question answering. Datasets such as SQuAD 2.0 & NQ have also introduced an auxiliary task requiring models to predict when a question has no answer in the text ...
Rishav Chakravarti, Avirup Sil
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Benchmarking Robustness of Machine Reading Comprehension Models [PDF]
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021 Machine Reading Comprehension (MRC) is an important testbed for evaluating models' natural language understanding (NLU) ability. There has been rapid progress in this area, with new models achieving impressive performance on various benchmarks. However, existing benchmarks only evaluate models on in-domain test sets without considering their robustness
Chenglei Si +5 more
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A Survey on Explainability in Machine Reading Comprehension
CoRR, 2020 This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). We present how the representation and inference challenges evolved and the steps which were taken to tackle these challenges. We also present the evaluation methodologies to assess the performance of explainable systems.
Mokanarangan Thayaparan +2 more
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Answer Extraction Method for Reading Comprehension Based on Frame Semantics and GraphStructure [PDF]
Jisuanji kexue, 2023 Machine reading comprehension is one of the most challenging tasks in the field of natural language processing.With the continuous development of deep learning technology and the release of large-scale MRC datasets,the performance of MRC models keep ...
YANG Zhizhuo, XU Lingling, Zhang Hu, LI Ru
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Benchmarking Machine Translation Efficacy for Teaching EAP Reading Comprehension Skills [PDF]
Fanāvarī-i āmūzish, 2023 Background and Objectives: Although Machine Translation (MT) is extensively researched within the field of Artificial Intelligence (AI) and translation studies, few studies have attempted to implement MT output in foreign language teaching (FLT).
V. Mirzaeian, M. Maghsoudi
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A Survey on Neural Machine Reading Comprehension
CoRR, 2019 Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge. In recent years, the popularity of deep learning and the establishment of large-scale datasets have both promoted the prosperity of Machine Reading Comprehension.
Boyu Qiu +3 more
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