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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 191–200 of 555 papers

TitleStatusHype
Have You Seen That Number? Investigating Extrapolation in Question Answering Models—0
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations—0
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction—0
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations—0
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading ComprehensionCode0
What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension—0
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark—0
Know your tools well: Better and faster QA with synthetic examples—0
A Unified Abstractive Model for Generating Question-Answer Pairs—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
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