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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 11–20 of 555 papers

TitleStatusHype
Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading ComprehensionCode1
Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading ComprehensionCode1
NorQuAD: Norwegian Question Answering DatasetCode1
Context-faithful Prompting for Large Language ModelsCode1
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading ComprehensionCode1
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named EntitiesCode1
Multitask Pre-training of Modular Prompt for Chinese Few-Shot LearningCode1
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair ExtractionCode1
End-to-End Chinese Speaker IdentificationCode1
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