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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 301–310 of 555 papers

TitleStatusHype
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-trainingCode0
Improving Cross-Lingual Reading Comprehension with Self-Training—0
NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer—0
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension—0
Conversational Machine Reading Comprehension for Vietnamese Healthcare TextsCode0
MRCBert: A Machine Reading ComprehensionApproach for Unsupervised SummarizationCode0
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension—0
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
Towards Robust Neural Retrieval Models with Synthetic Pre-Training—0
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?Code0
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