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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 526550 of 555 papers

TitleStatusHype
Document Modeling with External Attention for Sentence ExtractionCode0
Subword-augmented Embedding for Cloze Reading ComprehensionCode0
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task0
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
CLUF: a Neural Model for Second Language Acquisition Modeling0
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task0
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding0
Towards Inference-Oriented Reading Comprehension: ParallelQA0
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification0
BioRead: A New Dataset for Biomedical Reading ComprehensionCode0
Towards AMR-BR: A SemBank for Brazilian Portuguese Language0
CliCR: A Dataset of Clinical Case Reports for Machine Reading ComprehensionCode0
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text0
Adversarial reading networks for machine comprehension0
Stochastic Answer Networks for Machine Reading ComprehensionCode0
Dynamic Fusion Networks for Machine Reading Comprehension0
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world ApplicationsCode0
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation0
Dataset for the First Evaluation on Chinese Machine Reading ComprehensionCode0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability0
A Constituent-Centric Neural Architecture for Reading Comprehension0
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension0
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