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
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse StructureCode1
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading ComprehensionCode1
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple DocumentsCode1
Multitask Pre-training of Modular Prompt for Chinese Few-Shot LearningCode1
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation ReasoningCode1
ReCO: A Large Scale Chinese Reading Comprehension Dataset on OpinionCode1
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question AnsweringCode1
Retrospective Reader for Machine Reading ComprehensionCode1
RoR: Read-over-Read for Long Document Machine Reading ComprehensionCode1
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
SemEval-2021 Task 4: Reading Comprehension of Abstract MeaningCode1
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical ReasoningCode1
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
A Unified MRC Framework for Named Entity RecognitionCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
CodeQA: A Question Answering Dataset for Source Code ComprehensionCode1
ComQA:Compositional Question Answering via Hierarchical Graph Neural NetworksCode1
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair ExtractionCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
TREC CAsT 2019: The Conversational Assistance Track OverviewCode1
Show:102550
← PrevPage 3 of 23Next →

No leaderboard results yet.