Papers by Hoang Thanh-Tung
Diffusion Directed Acyclic Transformer for Non-Autoregressive Machine Translation (2025.acl-short)
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| Challenge: | Non-autoregressive transformers (NATs) often encounter performance challenges due to the multi-modality problem. |
| Approach: | They propose a direct-acyclic transformer (DAT) that captures multiple translation modalities to paths in a Directed Acyclic Graph (DAG) this allows the model to integrate latent variables into the model, which is crucial for DAT to achieve state-of-the-art performance. |
| Outcome: | The proposed model captures multiple translation modalities to paths in a Directed Acyclic Graph (DAG) but the collaboration with the latent variable introduced through the Glancing training is crucial for the model to attain state-of-the-art performance. |
Class based Influence Functions for Error Detection (2023.acl-short)
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Thang Nguyen-Duc, Hoang Thanh-Tung, Quan Hung Tran, Dang Huu-Tien, Hieu Nguyen, Anh T. V. Dau, Nghi Bui
| Challenge: | Influence functions (IFs) are powerful tools for detecting anomalous examples in large scale datasets. |
| Approach: | They propose a method to explain the instability of IFs by leveraging class information to improve the stability of ifs. |
| Outcome: | The proposed method improves performance and stability while incurring no additional computational cost. |
FastDiSS: Few-step Match Many-step Diffusion Language Model on Sequence-to-Sequence Generation (2026.findings-acl)
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| Challenge: | Existing models that correct errors in the model but lack a high quality of output . a novel training framework that matches inference noise to the model's inference signal improves performance . |
| Approach: | They propose a training framework that matches inference noise to model errors by perturbing the self-conditioning signal to match inference errors. |
| Outcome: | The proposed framework surpasses standard continuous diffusion models while providing 400x faster inference speed. |