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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 161–170 of 555 papers

TitleStatusHype
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
A Survey on Neural Machine Reading Comprehension—0
Attention-Guided Answer Distillation for Machine Reading Comprehension—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Deep Understanding based Multi-Document Machine Reading Comprehension—0
2DP-2MRC: 2-Dimensional Pointer-based Machine Reading Comprehension Method for Multimodal Moment Retrieval—0
A Unified Abstractive Model for Generating Question-Answer Pairs—0
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications—0
Effective Character-augmented Word Embedding for Machine Reading Comprehension—0
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