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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 181–190 of 555 papers

TitleStatusHype
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering—0
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change—0
A New Entity Extraction Method Based on Machine Reading Comprehension—0
Dual Co-Matching Network for Multi-choice Reading Comprehension—0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets—0
Dual Multi-head Co-attention for Multi-choice Reading Comprehension—0
Bi-directional CognitiveThinking Network for Machine Reading Comprehension—0
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications—0
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training—0
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