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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 301–325 of 555 papers

TitleStatusHype
Machine Reading Comprehension with Enhanced Linguistic Verifiers—0
ChemistryQA: A Complex Question Answering Dataset from Chemistry—0
Coreference Reasoning in Machine Reading ComprehensionCode0
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
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
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension—0
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian—0
A Multilingual Reading Comprehension System for more than 100 Languages—0
Multi-choice Relational Reasoning for Machine Reading Comprehension—0
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension—0
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation—0
Graph-Based Knowledge Integration for Question Answering over Dialogue—0
SQL Generation via Machine Reading ComprehensionCode0
Bi-directional CognitiveThinking Network for Machine Reading Comprehension—0
Robust Machine Reading Comprehension by Learning Soft labels—0
A Vietnamese Dataset for Evaluating Machine Reading Comprehension—0
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension—0
MRC Examples Answerable by BERT without a Question Are Less Effective in MRC Model Training—0
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection—0
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