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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 281–290 of 555 papers

TitleStatusHype
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
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0—0
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