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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 101–150 of 555 papers

TitleStatusHype
Rethinking Annotation: Can Language Learners Contribute?—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension—0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts—0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.—0
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond—0
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity RecogitionCode0
Aspect-based Sentiment Analysis as Machine Reading Comprehension—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
DIFM:An effective deep interaction and fusion model for sentence matching—0
基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)—0
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)—0
Robust Domain Adaptation for Machine Reading Comprehension—0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair ExtractionCode1
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension—0
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data—0
Large-scale Multi-granular Concept Extraction Based on Machine Reading ComprehensionCode0
Trigger-free Event Detection via Derangement Reading Comprehension—0
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension—0
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation—0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning—0
MRCLens: an MRC Dataset Bias Detection Toolkit—0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
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 Answering—0
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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