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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 51–60 of 555 papers

TitleStatusHype
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language VariantsCode2
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
ZeQR: Zero-shot Query Reformulation for Conversational SearchCode0
Teach model to answer questions after comprehending the document—0
IDOL: Indicator-oriented Logic Pre-training for Logical ReasoningCode1
SciMRC: Multi-perspective Scientific Machine Reading Comprehension—0
Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading ComprehensionCode1
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity LinkingCode0
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