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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 291–300 of 555 papers

TitleStatusHype
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension—0
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data—0
Semantics-Aware Inferential Network for Natural Language Understanding—0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management—0
Sentence Extraction-Based Machine Reading Comprehension for Vietnamese—0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation—0
SG-Net: Syntax Guided Transformer for Language Representation—0
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension—0
SkillQG: Learning to Generate Question for Reading Comprehension Assessment—0
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension—0
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