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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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TitleStatusHype
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations—0
Answer Generation through Unified Memories over Multiple Passages—0
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets—0
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming—0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability—0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering—0
Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation—0
EveMRC: A Two-stage Evidence Modeling For Multi-choice Machine Reading Comprehension—0
Event Detection via Derangement Reading Comprehension—0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
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