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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 431–440 of 555 papers

TitleStatusHype
Ask to Learn: A Study on Curiosity-driven Question Generation—0
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension—0
Dice Loss for Data-imbalanced NLP TasksCode0
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations—0
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations—0
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading ComprehensionCode0
Relation Module for Non-Answerable Predictions on Reading Comprehension—0
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network—0
Cross-Task Knowledge Transfer for Query-Based Text Summarization—0
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks—0
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