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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 555 papers

TitleStatusHype
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language VariantsCode2
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehensionCode0
Single-Sentence Reader: A Novel Approach for Addressing Answer Position BiasCode0
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model—0
Teach model to answer questions after comprehending the document—0
ZeQR: Zero-shot Query Reformulation for Conversational SearchCode0
IDOL: Indicator-oriented Logic Pre-training for Logical ReasoningCode1
Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading ComprehensionCode1
SciMRC: Multi-perspective Scientific Machine Reading Comprehension—0
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity LinkingCode0
Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading ComprehensionCode1
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language ModelsCode0
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite—0
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User ManualsCode0
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading ComprehensionCode0
A Causal View of Entity Bias in (Large) Language ModelsCode0
Machine Reading Comprehension using Case-based Reasoning—0
mPMR: A Multilingual Pre-trained Machine Reader at ScaleCode0
EMBRACE: Evaluation and Modifications for Boosting RACECode0
SkillQG: Learning to Generate Question for Reading Comprehension Assessment—0
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension—0
Adaptive loose optimization for robust question answeringCode0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
NorQuAD: Norwegian Question Answering DatasetCode1
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups—0
Show:102550
← PrevPage 3 of 23Next →

No leaderboard results yet.