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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 41–50 of 555 papers

TitleStatusHype
Mirror: A Universal Framework for Various Information Extraction TasksCode1
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading ComprehensionCode1
Multi-grained Evidence Inference for Multi-choice Reading Comprehension—0
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut TriggersCode0
Explaining Interactions Between Text SpansCode0
Instructive Dialogue Summarization with Query AggregationsCode0
Hierarchical Evaluation Framework: Best Practices for Human Evaluation—0
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning ApproachCode0
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change—0
Multi-turn Dialogue Comprehension from a Topic-aware Perspective—0
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