Papers by Duc Nguyen
Improving Multimodal Sentiment Analysis: Supervised Angular margin-based Contrastive Learning for Enhanced Fusion Representation (2023.findings-emnlp)
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| Challenge: | Existing methods for multimodal sentiment analysis focus on forming positive and negative pairs, neglecting the variation in sentiment scores within the same class. |
| Approach: | They propose a framework to enhance discrimination and generalizability of the multimodal representation and overcome biases in the fusion vector’s modality. |
| Outcome: | The proposed model improves discrimination and generalizability of the multimodal representation and overcomes biases in the fusion vector’s modality. |
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models (2024.findings-naacl)
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| Challenge: | Existing open-source LLMs exhibit limited effectiveness in processing Vietnamese . lack of systematic benchmark datasets and metrics tailored for Vietnamese LLM evaluation exacerbates these issues. |
| Approach: | They propose to fine tune LLMs specifically for Vietnamese and develop a framework for evaluation . they find that larger models introduce more biases and uncalibrated outputs . |
| Outcome: | The proposed framework finetunes LLMs specifically for Vietnamese and provides a framework for evaluation . |
NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization (2024.findings-emnlp)
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| Challenge: | Recent advances in neural topic models have focused on two main directions: the integration of the inference network with a pre-trained language model and the modeling of the relationship between words and topics in the generative model. |
| Approach: | They propose a framework to maximize mutual information between topics and PLMs . Experimental results indicate that NeuroMax reduces inference time . |
| Outcome: | The proposed framework reduces inference time, generates more coherent topics and topic groups, and produces more representative document embeddings. |
Introducing a New Dataset for Event Detection in Cybersecurity Texts (2020.emnlp-main)
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| Challenge: | a large amount of text data is produced to report and discuss cyber vulnerabilities . detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text. |
| Approach: | They propose a dataset characterizing the manual annotation for 30 important cybersecurity event types and a large dataset to develop deep learning models. |
| Outcome: | The proposed dataset characterizes the manual annotation for 30 important event types and supports the modeling of document-level information to improve the performance. |