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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 326–350 of 555 papers

TitleStatusHype
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension—0
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning—0
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
What does BERT Learn from Arabic Machine Reading Comprehension Datasets?—0
What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension—0
What is Missing in Existing Multi-hop Datasets? Toward Deeper Multi-hop Reasoning Task—0
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types—0
Why can't memory networks read effectively?—0
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts—0
XCMRC: Evaluating Cross-lingual Machine Reading Comprehension—0
XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source—0
Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches—0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble—0
Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization—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
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