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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 161–170 of 555 papers

TitleStatusHype
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics—0
Feature-augmented Machine Reading Comprehension with Auxiliary Tasks—0
IDK-MRC: Unanswerable Questions for Indonesian Machine Reading ComprehensionCode0
Rethinking Annotation: Can Language Learners Contribute?—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension—0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)—0
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)—0
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