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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 176–200 of 555 papers

TitleStatusHype
Robust Domain Adaptation for Machine Reading Comprehension—0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
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
An Understanding-Oriented Robust Machine Reading Comprehension ModelCode0
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
OPERA: Operation-Pivoted Discrete Reasoning over TextCode0
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
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
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
Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT—0
Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an—0
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension—0
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