Results 51 to 60 of about 8,093 (292)
Sogou Machine Reading Comprehension Toolkit
CoRR, 2019 Machine reading comprehension have been intensively studied in recent years, and neural network-based models have shown dominant performances. In this paper, we present a Sogou Machine Reading Comprehension (SMRC) toolkit that can be used to provide the fast and efficient development of modern machine comprehension models, including both published ...
Jindou Wu +7 more
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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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Knowledge Based Machine Reading Comprehension
CoRR, 2018 Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world. However, existing datasets are typically dominated by questions that can be well solved by context matching, which fail to test this capability.
Yibo Sun +6 more
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Controlled language and readability [PDF]
, 2010 Controlled Language (CL) rules specify constraints on lexicon, grammar and style with the objective of improving text translatability, comprehensibility, readability and usability.
O'Brien, Sharon +2 more
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Continual Domain Adaptation for Machine Reading Comprehension [PDF]
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020 Machine reading comprehension (MRC) has become a core component in a variety of natural language processing (NLP) applications such as question answering and dialogue systems. It becomes a practical challenge that an MRC model needs to learn in non-stationary environments, in which the underlying data distribution changes over time.
Lixin Su +5 more
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Cooperative Self-training of Machine Reading Comprehension
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022 Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. However, training question answering models still requires large amounts of annotated data for specific domains.
Hongyin Luo +4 more
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DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension
Transactions of the Association for Computational Linguistics, 2019 We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our data ...
Sun, Kai +5 more
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Improving Machine Reading Comprehension with Multi-Task Learning and Self-Training
Mathematics, 2022 Machine Reading Comprehension (MRC) is an AI challenge that requires machines to determine the correct answer to a question based on a given passage, in which extractive MRC requires extracting an answer span to a question from a given passage, such as ...
Jianquan Ouyang, Mengen Fu
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NumNet: Machine Reading Comprehension with Numerical Reasoning [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 Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human's reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems. To address this issue, we propose a numerical MRC model named as NumNet, which utilizes a numerically-aware graph neural network to ...
Qiu Ran +4 more
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A Survey on Machine Reading Comprehension—Tasks, Evaluation Metrics and Benchmark Datasets
Applied Sciences, 2020 Machine Reading Comprehension (MRC) is a challenging Natural Language Processing (NLP) research field with wide real-world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep ...
Changchang Zeng +4 more
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