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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 91–100 of 555 papers

TitleStatusHype
Rethinking Label Smoothing on Multi-hop Question AnsweringCode0
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension—0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches—0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics—0
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
Feature-augmented Machine Reading Comprehension with Auxiliary Tasks—0
IDK-MRC: Unanswerable Questions for Indonesian Machine Reading ComprehensionCode0
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named EntitiesCode1
Multitask Pre-training of Modular Prompt for Chinese Few-Shot LearningCode1
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