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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 301–350 of 555 papers

TitleStatusHype
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-trainingCode0
Improving Cross-Lingual Reading Comprehension with Self-Training—0
NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer—0
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension—0
Conversational Machine Reading Comprehension for Vietnamese Healthcare TextsCode0
MRCBert: A Machine Reading ComprehensionApproach for Unsupervised SummarizationCode0
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension—0
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
Towards Robust Neural Retrieval Models with Synthetic Pre-Training—0
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?Code0
What does BERT Learn from Arabic Machine Reading Comprehension Datasets?—0
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents—0
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension—0
Robustly Optimized and Distilled Training for Natural Language Understanding—0
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension—0
OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach—0
Biomedical Question Answering: A Survey of Approaches and Challenges—0
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data—0
Modeling Context in Answer Sentence Selection Systems on a Latency Budget—0
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning—0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks—0
English Machine Reading Comprehension Datasets: A SurveyCode0
Towards Confident Machine Reading Comprehension—0
Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking—0
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering—0
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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