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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
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension—0
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
Semantics-Aware Inferential Network for Natural Language Understanding—0
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World ApplicationsCode0
Answer Generation through Unified Memories over Multiple Passages—0
Logic-Guided Data Augmentation and Regularization for Consistent Question AnsweringCode1
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response DetectionCode0
CLUE: A Chinese Language Understanding Evaluation BenchmarkCode2
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the GapCode1
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse StructureCode1
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