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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 26–50 of 555 papers

TitleStatusHype
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity RecognitionCode1
Context-faithful Prompting for Large Language ModelsCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical ReasoningCode1
A Unified MRC Framework for Named Entity RecognitionCode1
Dependency Parsing as MRC-based Span-Span PredictionCode1
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading ComprehensionCode1
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair ExtractionCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation ReasoningCode1
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
ExpMRC: Explainability Evaluation for Machine Reading ComprehensionCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmCode1
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
IDOL: Indicator-oriented Logic Pre-training for Logical ReasoningCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
An MRC Framework for Semantic Role LabelingCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
End-to-End Chinese Speaker IdentificationCode1
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