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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
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions—0
Cooperative Self-training of Machine Reading Comprehension—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Correcting the Misuse: A Method for the Chinese Idiom Cloze Test—0
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning—0
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora—0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
Cross-Task Knowledge Transfer for Query-Based Text Summarization—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension—0
Data Augmentation for Biomedical Factoid Question Answering—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Deep Understanding based Multi-Document Machine Reading Comprehension—0
Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT—0
Developing Dataset of Japanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension—0
Dialog State Tracking: A Neural Reading Comprehension Approach—0
DIFM:An effective deep interaction and fusion model for sentence matching—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
Does Structure Matter? Encoding Documents for Machine Reading Comprehension—0
Dual Co-Matching Network for Multi-choice Reading Comprehension—0
Dual Multi-head Co-attention for Multi-choice Reading Comprehension—0
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications—0
Dynamic Fusion Networks for Machine Reading Comprehension—0
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task—0
Effective Character-augmented Word Embedding for Machine Reading Comprehension—0
emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information—0
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training—0
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension—0
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations—0
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension—0
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning—0
Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems—0
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations—0
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming—0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability—0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering—0
Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation—0
EveMRC: A Two-stage Evidence Modeling For Multi-choice Machine Reading Comprehension—0
Event Detection via Derangement Reading Comprehension—0
Event Extraction as Machine Reading Comprehension—0
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension—0
Explicit Utilization of General Knowledge in Machine Reading Comprehension—0
Feature-augmented Machine Reading Comprehension with Auxiliary Tasks—0
Feeding What You Need by Understanding What You Learned—0
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation—0
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection—0
FQuAD: French Question Answering Dataset—0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension—0
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