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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 151–200 of 555 papers

TitleStatusHype
XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source—0
The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot TransferCode0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—0
Data Augmentation for Biomedical Factoid Question AnsweringCode0
Improving Zero-Shot Event Extraction via Sentence Simplification—0
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading ComprehensionCode1
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
Lite Unified Modeling for Discriminative Reading ComprehensionCode0
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension—0
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading ComprehensionCode1
Feeding What You Need by Understanding What You Learned—0
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension TaskCode0
Relational Surrogate Loss LearningCode1
Deep Understanding based Multi-Document Machine Reading Comprehension—0
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words—0
Using calibrator to improve robustness in Machine Reading Comprehension—0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
JaQuAD: Japanese Question Answering Dataset for Machine Reading ComprehensionCode1
A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
An MRC Framework for Semantic Role Labeling—0
Data Augmentation for Biomedical Factoid Question Answering—0
Event Detection via Derangement Reading Comprehension—0
Cooperative Self-training of Machine Reading Comprehension—0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management—0
OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data—0
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension—0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension—0
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency GraphCode0
MRCLens: an MRC Dataset Bias Detection Toolkit—0
EveMRC: A Two-stage Evidence Modeling For Multi-choice Machine Reading Comprehension—0
Models can use keywords to answer questions that human cannot—0
On the Robustness of Reading Comprehension Models to Entity Renaming—0
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types—0
Unsupervised Open-Domain Question Answering with Higher Answerability—0
Context-Paraphrase Enhanced Commonsense Question Answering—0
A Graph Fusion Approach to Cross-Lingual Machine Reading Comprehension—0
Understanding Attention in Machine Reading Comprehension—0
ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese—0
UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension—0
Have You Seen That Number? Investigating Extrapolation in Question Answering Models—0
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations—0
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction—0
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations—0
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading ComprehensionCode0
What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension—0
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark—0
Know your tools well: Better and faster QA with synthetic examples—0
A Unified Abstractive Model for Generating Question-Answer Pairs—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
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