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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 201–225 of 555 papers

TitleStatusHype
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents—0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model—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
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