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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 521–530 of 555 papers

TitleStatusHype
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
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