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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 111–120 of 555 papers

TitleStatusHype
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading ComprehensionCode0
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition—0
Towards Efficient Methods in Medical Question Answering using Knowledge Graph EmbeddingsCode0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
Towards Robust Text Retrieval with Progressive LearningCode0
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension—0
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
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