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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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TitleStatusHype
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine ReadingCode0
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
Why Machine Reading Comprehension Models Learn Shortcuts?Code1
Does Structure Matter? Encoding Documents for Machine Reading Comprehension—0
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering—0
THG: Transformer with Hyperbolic Geometry—0
SemEval-2021 Task 4: Reading Comprehension of Abstract MeaningCode1
NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension—0
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models—0
Fact-driven Logical Reasoning for Machine Reading ComprehensionCode1
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