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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 226–250 of 555 papers

TitleStatusHype
A New Entity Extraction Method Based on Machine Reading Comprehension—0
An Intelligent Recommendation-cum-Reminder System—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension—0
A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph—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
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
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
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
Automatic Task Requirements Writing Evaluation via Machine Reading ComprehensionCode0
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model—0
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension—0
Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems—0
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization—0
PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales—0
What is Missing in Existing Multi-hop Datasets? Toward Deeper Multi-hop Reasoning Task—0
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings—0
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