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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 476–500 of 555 papers

TitleStatusHype
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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