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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 171–180 of 555 papers

TitleStatusHype
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User ManualsCode0
Dice Loss for Data-imbalanced NLP TasksCode0
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and ResourcesCode0
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World ApplicationsCode0
Effective Subword Segmentation for Text ComprehensionCode0
English Machine Reading Comprehension Datasets: A SurveyCode0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading ComprehensionCode0
Dual Ask-Answer Network for Machine Reading ComprehensionCode0
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