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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

Showing 451475 of 555 papers

TitleStatusHype
MMM: Multi-stage Multi-task Learning for Multi-choice Reading ComprehensionCode0
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language ModelsCode0
Dataset for the First Evaluation on Chinese Machine Reading ComprehensionCode0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on NovelsCode0
EMBRACE: Evaluation and Modifications for Boosting RACECode0
Stochastic Answer Networks for SQuAD 2.0Code0
Data Augmentation for Biomedical Factoid Question AnsweringCode0
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response DetectionCode0
Cross-Lingual Question Answering over Knowledge Base as Reading ComprehensionCode0
Ellipsis Resolution as Question Answering: An EvaluationCode0
BioRead: A New Dataset for Biomedical Reading ComprehensionCode0
mPMR: A Multilingual Pre-trained Machine Reader at ScaleCode0
Cross-Lingual Machine Reading ComprehensionCode0
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine ComprehensionCode0
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency GraphCode0
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut TriggersCode0
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
MRCBert: A Machine Reading ComprehensionApproach for Unsupervised SummarizationCode0
MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension BenchmarkCode0
Hierarchical Attention: What Really Counts in Various NLP TasksCode0
BIOMRC: A Dataset for Biomedical Machine Reading ComprehensionCode0
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading ComprehensionCode0
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