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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 151–175 of 555 papers

TitleStatusHype
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionCode0
Explaining Interactions Between Text SpansCode0
Abstract, Rationale, Stance: A Joint Model for Scientific Claim VerificationCode0
Entity-Relation Extraction as Multi-Turn Question AnsweringCode0
Evidence Sentence Extraction for Machine Reading ComprehensionCode0
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World ApplicationsCode0
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
A Span-Extraction Dataset for Chinese Machine Reading ComprehensionCode0
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut TriggersCode0
Hierarchical Attention: What Really Counts in Various NLP TasksCode0
Data Augmentation for Biomedical Factoid Question AnsweringCode0
Effective Subword Segmentation for Text ComprehensionCode0
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language EducationCode0
Instructive Dialogue Summarization with Query AggregationsCode0
Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question AnsweringCode0
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world ApplicationsCode0
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
Dual Ask-Answer Network for Machine Reading ComprehensionCode0
Ellipsis Resolution as Question Answering: An EvaluationCode0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
Dice Loss for Data-imbalanced NLP TasksCode0
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading ComprehensionCode0
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
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