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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 301–350 of 555 papers

TitleStatusHype
To Test Machine Comprehension, Start by Defining Comprehension—0
Towards AMR-BR: A SemBank for Brazilian Portuguese Language—0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
Towards Confident Machine Reading Comprehension—0
Towards Inference-Oriented Reading Comprehension: ParallelQA—0
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text—0
Towards Robust Neural Retrieval Models with Synthetic Pre-Training—0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.—0
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering—0
Trigger-free Event Detection via Derangement Reading Comprehension—0
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension—0
Uncertainty-Based Adaptive Learning for Reading Comprehension—0
Understanding Attention in Machine Reading Comprehension—0
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data—0
Unsupervised Explanation Generation for Machine Reading Comprehension—0
Unsupervised Open-Domain Question Answering—0
Unsupervised Open-Domain Question Answering with Higher Answerability—0
UQuAD1.0: Development of an Urdu Question Answering Training Data for Machine Reading Comprehension—0
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models—0
Using calibrator to improve robustness in Machine Reading Comprehension—0
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension—0
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond—0
ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese—0
Visualizing attention zones in machine reading comprehension models—0
Visual Question Answering as Reading Comprehension—0
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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