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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 201–250 of 555 papers

TitleStatusHype
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question AnsweringCode0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model—0
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
OPERA:Operation-Pivoted Discrete Reasoning over Text—0
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
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
Feeding What You Need by Understanding What You Learned—0
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension TaskCode0
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
A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
Event Detection via Derangement Reading Comprehension—0
An MRC Framework for Semantic Role Labeling—0
Data Augmentation for Biomedical Factoid Question Answering—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
A Graph Fusion Approach to Cross-Lingual Machine Reading Comprehension—0
EveMRC: A Two-stage Evidence Modeling For Multi-choice Machine Reading Comprehension—0
ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese—0
Understanding Attention in Machine Reading Comprehension—0
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types—0
Models can use keywords to answer questions that human cannot—0
Unsupervised Open-Domain Question Answering with Higher Answerability—0
Context-Paraphrase Enhanced Commonsense Question Answering—0
On the Robustness of Reading Comprehension Models to Entity Renaming—0
UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension—0
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading ComprehensionCode0
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations—0
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations—0
Have You Seen That Number? Investigating Extrapolation in Question Answering Models—0
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction—0
What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension—0
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