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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 51–60 of 555 papers

TitleStatusHype
JaQuAD: Japanese Question Answering Dataset for Machine Reading ComprehensionCode1
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading ComprehensionCode1
Logic-Guided Data Augmentation and Regularization for Consistent Question AnsweringCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading ComprehensionCode1
MoEfication: Transformer Feed-forward Layers are Mixtures of ExpertsCode1
MS MARCO: A Human Generated MAchine Reading COmprehension DatasetCode1
CodeQA: A Question Answering Dataset for Source Code ComprehensionCode1
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