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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 141–150 of 555 papers

TitleStatusHype
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering—0
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical ReasoningCode1
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents—0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model—0
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
OPERA:Operation-Pivoted Discrete Reasoning over Text—0
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