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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
A Span-Extraction Dataset for Chinese Machine Reading ComprehensionCode0
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension—0
U-Net: Machine Reading Comprehension with Unanswerable QuestionsCode0
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
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller—0
Listening Comprehension over Argumentative Content—0
Answer-focused and Position-aware Neural Question Generation—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
A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension—0
Systematic Error Analysis of the Stanford Question Answering Dataset—0
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 Task—0
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task—0
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task—0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—0
Towards Inference-Oriented Reading Comprehension: ParallelQA—0
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification—0
BioRead: A New Dataset for Biomedical Reading ComprehensionCode0
Towards AMR-BR: A SemBank for Brazilian Portuguese Language—0
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
Dynamic Fusion Networks for Machine Reading Comprehension—0
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world ApplicationsCode0
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation—0
Dataset for the First Evaluation on Chinese Machine Reading ComprehensionCode0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability—0
A Constituent-Centric Neural Architecture for Reading Comprehension—0
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension—0
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