Duc-Trong Le

Also published as: Duc Trong Le


ReINTEL: A Multimodal Data Challenge for Responsible Information Identification on Social Network Sites
Duc-Trong Le | Xuan-Son Vu | Nhu-Dung To | Huu-Quang Nguyen | Thuy-Trinh Nguyen | Thi Khanh-Linh Le | Anh-Tuan Nguyen | Minh-Duc Hoang | Nghia Le | Huyen Nguyen | Hoang D. Nguyen
Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing

Multimodal Review Generation with Privacy and Fairness Awareness
Xuan-Son Vu | Thanh-Son Nguyen | Duc-Trong Le | Lili Jiang
Proceedings of the 28th International Conference on Computational Linguistics

Users express their opinions towards entities (e.g., restaurants) via online reviews which can be in diverse forms such as text, ratings, and images. Modeling reviews are advantageous for user behavior understanding which, in turn, supports various user-oriented tasks such as recommendation, sentiment analysis, and review generation. In this paper, we propose MG-PriFair, a multimodal neural-based framework, which generates personalized reviews with privacy and fairness awareness. Motivated by the fact that reviews might contain personal information and sentiment bias, we propose a novel differentially private (dp)-embedding model for training privacy guaranteed embeddings and an evaluation approach for sentiment fairness in the food-review domain. Experiments on our novel review dataset show that MG-PriFair is capable of generating plausibly long reviews while controlling the amount of exploited user data and using the least sentiment biased word embeddings. To the best of our knowledge, we are the first to bring user privacy and sentiment fairness into the review generation task. The dataset and source codes are available at https://github.com/ReML-AI/MG-PriFair.

Introducing a New Dataset for Event Detection in Cybersecurity Texts
Hieu Man Duc Trong | Duc Trong Le | Amir Pouran Ben Veyseh | Thuat Nguyen | Thien Huu Nguyen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text. In this work, we focus on the task of event detection (ED) to identify event trigger words for the cybersecurity domain. In particular, to facilitate the future research, we introduce a new dataset for this problem, characterizing the manual annotation for 30 important cybersecurity event types and a large dataset size to develop deep learning models. Comparing to the prior datasets for this task, our dataset involves more event types and supports the modeling of document-level information to improve the performance. We perform extensive evaluation with the current state-of-the-art methods for ED on the proposed dataset. Our experiments reveal the challenges of cybersecurity ED and present many research opportunities in this area for the future work.


A Model of Vietnamese Person Named Entity Question Answering System
Mai-Vu Tran | Duc-Trong Le | Xuan Tu Tran | Tien-Tung Nguyen
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation