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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
Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop—0
Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question AnsweringCode0
Adversarial Self-Attention for Language UnderstandingCode0
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions—0
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model—0
Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT—0
Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an—0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmCode1
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