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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 101–110 of 555 papers

TitleStatusHype
Rethinking Annotation: Can Language Learners Contribute?—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension—0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity RecogitionCode0
Aspect-based Sentiment Analysis as Machine Reading Comprehension—0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.—0
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond—0
DIFM:An effective deep interaction and fusion model for sentence matching—0
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