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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 151–160 of 555 papers

TitleStatusHype
A Unified Abstractive Model for Generating Question-Answer Pairs—0
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text—0
Dialog State Tracking: A Neural Reading Comprehension Approach—0
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model—0
Attention-Guided Answer Distillation for Machine Reading Comprehension—0
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension—0
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
A Survey on Neural Machine Reading Comprehension—0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks—0
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