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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 271–280 of 555 papers

TitleStatusHype
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Ti-Reader: 基于注意力机制的藏文机器阅读理解端到端网络模型(Ti-Reader: An End-to-End Network Model Based on Attention Mechanisms for Tibetan Machine Reading Comprehension)—0
Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain—0
Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension—0
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes—0
基于小句复合体的中文机器阅读理解研究(Machine Reading Comprehension Based on Clause Complex)—0
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications—0
Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction—0
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification—0
A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph—0
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