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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 121–130 of 555 papers

TitleStatusHype
Hierarchical Evaluation Framework: Best Practices for Human Evaluation—0
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning ApproachCode0
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change—0
Multi-turn Dialogue Comprehension from a Topic-aware Perspective—0
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehensionCode0
Single-Sentence Reader: A Novel Approach for Addressing Answer Position BiasCode0
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model—0
Teach model to answer questions after comprehending the document—0
ZeQR: Zero-shot Query Reformulation for Conversational SearchCode0
SciMRC: Multi-perspective Scientific Machine Reading Comprehension—0
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