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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 281–290 of 555 papers

TitleStatusHype
面向机器阅读理解的高质量藏语数据集构建(Construction of High-quality Tibetan Dataset for Machine Reading Comprehension)—0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation—0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision—0
Automatic Task Requirements Writing Evaluation via Machine Reading ComprehensionCode0
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
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
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