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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 326–350 of 555 papers

TitleStatusHype
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis—0
ChemistryQA: A Complex Question Answering Dataset from Chemistry—0
Learning to Generate Questions by Recovering Answer-containing Sentences—0
Machine Reading Comprehension with Enhanced Linguistic Verifiers—0
Uncertainty-Based Adaptive Learning for Reading Comprehension—0
Coreference Reasoning in Machine Reading ComprehensionCode0
SG-Net: Syntax Guided Transformer for Language Representation—0
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension—0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
Semantics Altering Modifications for Evaluating Comprehension in Machine ReadingCode0
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning—0
Reference Knowledgeable Network for Machine Reading ComprehensionCode0
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training—0
MRC Examples Answerable by BERT without a Question Are Less Effective in MRC Model Training—0
A Multilingual Reading Comprehension System for more than 100 Languages—0
A Vietnamese Dataset for Evaluating Machine Reading Comprehension—0
Bi-directional CognitiveThinking Network for Machine Reading Comprehension—0
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation—0
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection—0
Graph-Based Knowledge Integration for Question Answering over Dialogue—0
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension—0
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension—0
Multi-choice Relational Reasoning for Machine Reading Comprehension—0
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian—0
Robust Machine Reading Comprehension by Learning Soft labels—0
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