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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 271–280 of 555 papers

TitleStatusHype
MRCBert: A Machine Reading ComprehensionApproach for Unsupervised SummarizationCode0
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension—0
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation ReasoningCode1
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
Towards Robust Neural Retrieval Models with Synthetic Pre-Training—0
Connecting Attributions and QA Model Behavior on Realistic CounterfactualsCode1
What does BERT Learn from Arabic Machine Reading Comprehension Datasets?—0
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?Code0
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents—0
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension—0
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