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Machine Reading Comprehension

Machine Reading Comprehension is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.

Source: Making Neural Machine Reading Comprehension Faster

Papers

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Showing 241–250 of 555 papers

TitleStatusHype
Automatic Task Requirements Writing Evaluation via Machine Reading ComprehensionCode0
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model—0
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension—0
Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems—0
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization—0
PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales—0
What is Missing in Existing Multi-hop Datasets? Toward Deeper Multi-hop Reasoning Task—0
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings—0
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