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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 131–140 of 555 papers

TitleStatusHype
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity LinkingCode0
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language ModelsCode0
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite—0
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User ManualsCode0
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading ComprehensionCode0
Machine Reading Comprehension using Case-based Reasoning—0
A Causal View of Entity Bias in (Large) Language ModelsCode0
mPMR: A Multilingual Pre-trained Machine Reader at ScaleCode0
EMBRACE: Evaluation and Modifications for Boosting RACECode0
SkillQG: Learning to Generate Question for Reading Comprehension Assessment—0
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