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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 151–200 of 555 papers

TitleStatusHype
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and ResourcesCode0
Adaptive loose optimization for robust question answeringCode0
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User ManualsCode0
Large-scale Multi-granular Concept Extraction Based on Machine Reading ComprehensionCode0
Interactive Machine Comprehension with Information Seeking AgentsCode0
Interpreting Themes from Educational StoriesCode0
Cross-Lingual Question Answering over Knowledge Base as Reading ComprehensionCode0
Instructive Dialogue Summarization with Query AggregationsCode0
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?Code0
Cross-Lingual Machine Reading ComprehensionCode0
IDK-MRC: Unanswerable Questions for Indonesian Machine Reading ComprehensionCode0
Data Augmentation for Biomedical Factoid Question AnsweringCode0
Hierarchical Attention: What Really Counts in Various NLP TasksCode0
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading ComprehensionCode0
Lite Unified Modeling for Discriminative Reading ComprehensionCode0
Improving Machine Reading Comprehension with General Reading StrategiesCode0
Coreference Reasoning in Machine Reading ComprehensionCode0
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response DetectionCode0
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine ComprehensionCode0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
Conversational Machine Reading Comprehension for Vietnamese Healthcare TextsCode0
Abstract, Rationale, Stance: A Joint Model for Scientific Claim VerificationCode0
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut TriggersCode0
Document Modeling with External Attention for Sentence ExtractionCode0
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity RecogitionCode0
A Span-Extraction Dataset for Chinese Machine Reading ComprehensionCode0
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language EducationCode0
Dual Ask-Answer Network for Machine Reading ComprehensionCode0
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning ApproachCode0
Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question AnsweringCode0
Explaining Interactions Between Text SpansCode0
Evidence Sentence Extraction for Machine Reading ComprehensionCode0
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity LinkingCode0
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World ApplicationsCode0
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionCode0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading ComprehensionCode0
EQuANt (Enhanced Question Answer Network)Code0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading ComprehensionCode0
EMBRACE: Evaluation and Modifications for Boosting RACECode0
English Machine Reading Comprehension Datasets: A SurveyCode0
Entity-Relation Extraction as Multi-Turn Question AnsweringCode0
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