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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 76–100 of 555 papers

TitleStatusHype
Connecting Attributions and QA Model Behavior on Realistic CounterfactualsCode1
Fact-driven Logical Reasoning for Machine Reading ComprehensionCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
Interactive Language Learning by Question AnsweringCode1
ARES: A Reading Comprehension Ensembling Service—0
A Graph Fusion Approach to Cross-Lingual Machine Reading Comprehension—0
Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction—0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension—0
A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension—0
A Constituent-Centric Neural Architecture for Reading Comprehension—0
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension—0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches—0
App-Aware Response Synthesis for User Reviews—0
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
A Frame-based Sentence Representation for Machine Reading Comprehension—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
CFO: A Framework for Building Production NLP Systems—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
Answer Span Correction in Machine Reading Comprehension—0
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings—0
Answer Generation through Unified Memories over Multiple Passages—0
Answer-focused and Position-aware Neural Question Generation—0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics—0
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension—0
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