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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 121–130 of 555 papers

TitleStatusHype
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation—0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning—0
MRCLens: an MRC Dataset Bias Detection Toolkit—0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
End-to-End Chinese Speaker IdentificationCode1
OPERA: Operation-Pivoted Discrete Reasoning over TextCode0
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
An Understanding-Oriented Robust Machine Reading Comprehension ModelCode0
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