Jialin Yu's Outputs (7)

Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance (2023)
Presentation / Conference Contribution
Aduragba, T. O., Yu, J., Cristea, A. I., & Long, Y. (2023, April). Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance. Presented at WWW '23: The ACM Web Conference 2023, Austin, Texas

People often use disease or symptom terms on social media and online forums in ways other than to describe their health. Thus the NLP health mention classification (HMC) task aims to identify posts where users are discussing health conditions literal... Read More about Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance.

Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention (2022)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Al Moubayed, N., & Shi, L. (2022, December). Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention. Presented at IEEE Big Data, Osaka, Japan

Medical visual question answering (Med-VQA) is to answer medical questions based on clinical images provided. This field is still in its infancy due to the complexity of the trio formed of questions, multimodal features and expert knowledge. In this... Read More about Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention.

Efficient Uncertainty Quantification for Multilabel Text Classification (2022)
Presentation / Conference Contribution
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). Efficient Uncertainty Quantification for Multilabel Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty qua... Read More about Efficient Uncertainty Quantification for Multilabel Text Classification.

Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification (2022)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022, July). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

Graph neural networks (GNNs) have attracted extensive interest in text classification tasks due to their expected superior performance in representation learning. However, most existing studies adopted the same semi-supervised learning setting as the... Read More about Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification.

INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations (2022)
Presentation / Conference Contribution
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decisionmaking, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a s... Read More about INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations.

A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data (2021)
Presentation / Conference Contribution
Sun, Z., Harit, A., Yu, J., Cristea, A., & Al Moubayed, N. (2021, July). A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data. Presented at IEEE International Joint Conference on Neural Network (IJCNN2021), Virtual

This research focuses on semi-supervised classification tasks, specifically for graph-structured data under datascarce situations. It is known that the performance of conventional supervised graph convolutional models is mediocre at classification ta... Read More about A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data.

Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums (2021)
Presentation / Conference Contribution
Yu, J., Alrajhi, L., Harit, A., Sun, Z., Cristea, A. I., & Shi, L. (2021, June). Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums. Presented at Intelligent Tutoring Systems, Athens, Greece / Virtual

Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. However, due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support. Learners may post the... Read More about Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums.