Papers by Thanh Tran
Unmasking Fake Careers: Detecting Machine-Generated Career Trajectories via Multi-layer Heterogeneous Graphs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) generate convincing career trajectories in fake resumes . a novel heterogeneous, hierarchical multi-layer graph framework is proposed to model career entities and their relations in a unified global graph built from genuine resumes. |
| Approach: | They propose a novel heterogeneous, hierarchical multi-layer graph framework that models career entities and their relations in a unified global graph built from genuine resumes. |
| Outcome: | The proposed framework outperforms state-of-the-art models by 5.8-85.0% relative to baselines. |
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing frameworks for guiding a language model in reasoning tasks are limited by their tendency to generate low-quality rationales that are repetitive and vacuous. |
| Approach: | They propose a framework that leverages a lightweight language model for guiding a black-box large LM in reasoning tasks. |
| Outcome: | The proposed framework outperforms baselines in answer prediction accuracy. |
HABERTOR: An Efficient and Effective Deep Hatespeech Detector (2020.emnlp-main)
Copied to clipboard
| Challenge: | HABERTOR model is a highly efficient and effective alternative to BERT for the hatespeech classification task. |
| Approach: | They propose to modify BERT's HABERTOR model to generate its own vocabularies and pre-trained it using the largest scale hatespeech dataset. |
| Outcome: | The proposed model is faster, more efficient and more robust than existing methods for hatespeech classification. |
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation (2025.emnlp-main)
Copied to clipboard
Khai Le-Duc, Tuyen Tran, Bach Phan Tat, Nguyen Kim Hai Bui, Quan Dang Anh, Hung-Phong Tran, Thanh Thuy Nguyen, Ly Nguyen, Tuan Minh Phan, Thi Thu Phuong Tran, Chris Ngo, Khanh Xuan Nguyen, Thanh Nguyen-Tang
| Challenge: | Multilingual speech translation (ST) and machine translation (MT) in the medical domain enhances patient care by enabling efficient communication across language barriers. |
| Approach: | They present a large-scale ST dataset for the medical domain spanning all translation directions in Vietnamese, English, German, French, and Simplified/Traditional Chinese, together with the models. |
| Outcome: | The multi-language speech translation (ST) and machine translation (MT) in the medical domain is the largest medical MT dataset and the largest many-to-many multilingual ST among all domains. |
Preserving Generalization of Language models in Few-shot Continual Relation Extraction (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for Few-shot Continual Relations Extraction (FCRE) are limited in labeled training data and models must learn from a few new samples to solve new tasks. |
| Approach: | They propose a method that leverages often-discarded language model heads to integrate knowledge from new relations with limited labeled data while avoiding catastrophic forgetting. |
| Outcome: | The proposed method circumvents catastrophic forgetting and preserves prior knowledge from pre-trained backbones while maintaining accuracy of existing classifications. |
SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking (2026.acl-industry)
Copied to clipboard
| Challenge: | Existing methods struggle with semantic ambiguity, homonyms, and complex linguistic structures, often trading accuracy for efficiency. |
| Approach: | They propose a Vietnamese fact-checking framework that integrates SER and TVC to achieve 78.97% strict accuracy. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy with 78.97% strict accuracy on ISE-DSC01 and 80.82% on ViWikiFC while maintaining competitive accuracy. |