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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 451–500 of 555 papers

TitleStatusHype
基於BERT模型之多國語言機器閱讀理解研究(Multilingual Machine Reading Comprehension based on BERT Model)—0
Integrated Triaging for Fast Reading Comprehension—0
Improving Pre-Trained Multilingual Models with Vocabulary Expansion—0
ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering—0
KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension—0
Symmetric Regularization based BERT for Pair-wise Semantic ReasoningCode0
Semantics-aware BERT for Language UnderstandingCode0
Cross-Lingual Machine Reading ComprehensionCode0
Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension—0
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning—0
Ellipsis Resolution as Question Answering: An EvaluationCode0
Interactive Machine Comprehension with Information Seeking AgentsCode0
Adversarial Domain Adaptation for Machine Reading Comprehension—0
Query-Based Named Entity Recognition—0
CFO: A Framework for Building Production NLP Systems—0
XCMRC: Evaluating Cross-lingual Machine Reading Comprehension—0
SG-Net: Syntax-Guided Machine Reading ComprehensionCode0
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning—0
Dialog State Tracking: A Neural Reading Comprehension Approach—0
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine ComprehensionCode0
Tackling Graphical NLP problems with Graph Recurrent NetworksCode0
An Effective Multi-Stage Approach For Question Answering—0
Neural Machine Reading Comprehension: Methods and Trends—0
CALOR-QUEST : un corpus d'entra\^ et d'\'evaluation pour la compr\'ehension automatique de textes (Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document)—0
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading ComprehensionCode0
MC\^2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension—0
Machine Reading Comprehension: a Literature Review—0
EQuANt (Enhanced Question Answer Network)Code0
Structured Pruning of Recurrent Neural Networks through Neuron Selection—0
Learning to Ask Unanswerable Questions for Machine Reading Comprehension—0
Neural Arabic Question AnsweringCode0
A Survey on Neural Machine Reading Comprehension—0
Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches—0
Controlling Risk of Web Question Answering—0
Entity-Relation Extraction as Multi-Turn Question AnsweringCode0
Investigating Prior Knowledge for Challenging Chinese Machine Reading ComprehensionCode0
Making Neural Machine Reading Comprehension Faster—0
Sogou Machine Reading Comprehension ToolkitCode0
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented GraphsCode0
Evidence Sentence Extraction for Machine Reading ComprehensionCode0
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots—0
Review Conversational Reading ComprehensionCode0
Dual Co-Matching Network for Multi-choice Reading Comprehension—0
SDNet: Contextualized Attention-based Deep Network for Conversational Question AnsweringCode0
未登錄詞之向量表示法模型於中文機器閱讀理解之應用 (An OOV Word Embedding Framework for Chinese Machine Reading Comprehension)—0
Visual Question Answering as Reading Comprehension—0
A Deep Cascade Model for Multi-Document Reading Comprehension—0
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions—0
Effective Subword Segmentation for Text ComprehensionCode0
Improving Machine Reading Comprehension with General Reading StrategiesCode0
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