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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 231–240 of 555 papers

TitleStatusHype
面向机器阅读理解的高质量藏语数据集构建(Construction of High-quality Tibetan Dataset for Machine Reading Comprehension)—0
Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension—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
Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain—0
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification—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
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes—0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation—0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision—0
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