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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 331–340 of 555 papers

TitleStatusHype
What is Missing in Existing Multi-hop Datasets? Toward Deeper Multi-hop Reasoning Task—0
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types—0
Why can't memory networks read effectively?—0
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts—0
XCMRC: Evaluating Cross-lingual Machine Reading Comprehension—0
XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source—0
Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches—0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble—0
Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization—0
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming—0
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