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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 126–150 of 555 papers

TitleStatusHype
End-to-End Chinese Speaker IdentificationCode1
OPERA: Operation-Pivoted Discrete Reasoning over TextCode0
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
An Understanding-Oriented Robust Machine Reading Comprehension ModelCode0
Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop—0
Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question AnsweringCode0
Adversarial Self-Attention for Language UnderstandingCode0
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions—0
Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an—0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model—0
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT—0
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmCode1
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question AnsweringCode0
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical ReasoningCode1
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
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
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
OPERA:Operation-Pivoted Discrete Reasoning over Text—0
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