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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 501–550 of 555 papers

TitleStatusHype
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension—0
A Span-Extraction Dataset for Chinese Machine Reading ComprehensionCode0
U-Net: Machine Reading Comprehension with Unanswerable QuestionsCode0
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension—0
Listening Comprehension over Argumentative Content—0
Answer-focused and Position-aware Neural Question Generation—0
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller—0
未登錄詞之向量表示法模型於中文機器閱讀理解之應用 (An OOV Word Embedding Framework for Chinese Machine Reading Comprehension) [In Chinese]—0
A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension—0
Stochastic Answer Networks for SQuAD 2.0Code0
Multi-task Learning with Sample Re-weighting for Machine Reading ComprehensionCode0
Knowledge Based Machine Reading Comprehension—0
Explicit Utilization of General Knowledge in Machine Reading Comprehension—0
Explicit Contextual Semantics for Text Comprehension—0
Dual Ask-Answer Network for Machine Reading ComprehensionCode0
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension—0
What Makes Reading Comprehension Questions Easier?Code0
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading ComprehensionCode0
Attention-Guided Answer Distillation for Machine Reading Comprehension—0
Read + Verify: Machine Reading Comprehension with Unanswerable Questions—0
Hierarchical Attention: What Really Counts in Various NLP TasksCode0
Effective Character-augmented Word Embedding for Machine Reading Comprehension—0
Learning Semantic Sentence Embeddings using Sequential Pair-wise DiscriminatorCode0
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension—0
Systematic Error Analysis of the Stanford Question Answering Dataset—0
Document Modeling with External Attention for Sentence ExtractionCode0
A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension—0
Subword-augmented Embedding for Cloze Reading ComprehensionCode0
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task—0
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble—0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—0
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task—0
Towards Inference-Oriented Reading Comprehension: ParallelQA—0
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification—0
Towards AMR-BR: A SemBank for Brazilian Portuguese Language—0
BioRead: A New Dataset for Biomedical Reading ComprehensionCode0
CliCR: A Dataset of Clinical Case Reports for Machine Reading ComprehensionCode0
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text—0
Adversarial reading networks for machine comprehension—0
Stochastic Answer Networks for Machine Reading ComprehensionCode0
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world ApplicationsCode0
Dynamic Fusion Networks for Machine Reading Comprehension—0
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation—0
Dataset for the First Evaluation on Chinese Machine Reading ComprehensionCode0
A Constituent-Centric Neural Architecture for Reading Comprehension—0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability—0
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