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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 21–30 of 555 papers

TitleStatusHype
End-to-End Chinese Speaker IdentificationCode1
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmCode1
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical ReasoningCode1
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading ComprehensionCode1
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading ComprehensionCode1
Relational Surrogate Loss LearningCode1
JaQuAD: Japanese Question Answering Dataset for Machine Reading ComprehensionCode1
On the Robustness of Reading Comprehension Models to Entity RenamingCode1
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
MoEfication: Transformer Feed-forward Layers are Mixtures of ExpertsCode1
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