A review of deep-neural automated essay scoring models
M Uto - Behaviormetrika, 2021 - Springer
Automated essay scoring (AES) is the task of automatically assigning scores to essays as an
alternative to grading by humans. Although traditional AES models typically rely on manually …
alternative to grading by humans. Although traditional AES models typically rely on manually …
Selective-supervised contrastive learning with noisy labels
Deep networks have strong capacities of embedding data into latent representations and
finishing following tasks. However, the capacities largely come from high-quality annotated …
finishing following tasks. However, the capacities largely come from high-quality annotated …
Knowledge learning with crowdsourcing: A brief review and systematic perspective
J Zhang - IEEE/CAA Journal of Automatica Sinica, 2022 - ieeexplore.ieee.org
Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity,
and uncertainty, which lead the knowledge learning from them full of challenges. With the …
and uncertainty, which lead the knowledge learning from them full of challenges. With the …
Estimating noise transition matrix with label correlations for noisy multi-label learning
In label-noise learning, the noise transition matrix, bridging the class posterior for noisy and
clean data, has been widely exploited to learn statistically consistent classifiers. The …
clean data, has been widely exploited to learn statistically consistent classifiers. The …
Employing multi-estimations for weakly-supervised semantic segmentation
Image-level label based weakly-supervised semantic segmentation (WSSS) aims to adopt
image-level labels to train semantic segmentation models, saving vast human labors for …
image-level labels to train semantic segmentation models, saving vast human labors for …
Coupled confusion correction: Learning from crowds with sparse annotations
As the size of the datasets getting larger, accurately annotating such datasets is becoming
more impractical due to the expensiveness on both time and economy. Therefore, crowd …
more impractical due to the expensiveness on both time and economy. Therefore, crowd …
Sport: A subgraph perspective on graph classification with label noise
Graph neural networks (GNNs) have achieved great success recently on graph classification
tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels …
tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels …
Beyond confusion matrix: learning from multiple annotators with awareness of instance features
Learning from multiple annotators aims to induce a high-quality classifier from training
instances, where each of them is associated with a set of observed labels provided by …
instances, where each of them is associated with a set of observed labels provided by …
Learning automated essay scoring models using item-response-theory-based scores to decrease effects of rater biases
M Uto, M Okano - IEEE Transactions on Learning …, 2021 - ieeexplore.ieee.org
In automated essay scoring (AES), scores are automatically assigned to essays as an
alternative to grading by humans. Traditional AES typically relies on handcrafted features …
alternative to grading by humans. Traditional AES typically relies on handcrafted features …
Integration of prediction scores from various automated essay scoring models using item response theory
M Uto, I Aomi, E Tsutsumi… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In automated essay scoring (AES), essays are automatically graded without human raters.
Many AES models based on various manually designed features or various architectures of …
Many AES models based on various manually designed features or various architectures of …