SOTAVerified

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

Showing 76100 of 555 papers

TitleStatusHype
MoEfication: Transformer Feed-forward Layers are Mixtures of ExpertsCode1
TSQA: Tabular Scenario Based Question AnsweringCode1
ComQA:Compositional Question Answering via Hierarchical Graph Neural NetworksCode1
Coreference Resolution as Query-based Span PredictionCode1
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response DetectionCode0
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine ComprehensionCode0
A Framework for Evaluation of Machine Reading Comprehension Gold StandardsCode0
An Understanding-Oriented Robust Machine Reading Comprehension ModelCode0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut TriggersCode0
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionCode0
Evidence Sentence Extraction for Machine Reading ComprehensionCode0
Explaining Interactions Between Text SpansCode0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
Adversarial Self-Attention for Language UnderstandingCode0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
Entity-Relation Extraction as Multi-Turn Question AnsweringCode0
English Machine Reading Comprehension Datasets: A SurveyCode0
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading ComprehensionCode0
EQuANt (Enhanced Question Answer Network)Code0
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension TaskCode0
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
Show:102550
← PrevPage 4 of 23Next →

No leaderboard results yet.