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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 521–530 of 555 papers

TitleStatusHype
Improving Pre-Trained Multilingual Model with Vocabulary Expansion—0
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior—0
Improving Zero-Shot Event Extraction via Sentence Simplification—0
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension—0
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning—0
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups—0
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension—0
Integrated Triaging for Fast Reading Comprehension—0
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