Papers by Cong Dao Tran
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. |
ViDeBERTa: A powerful pre-trained language model for Vietnamese (2023.findings-eacl)
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| Challenge: | Existing models for Vietnamese that perform well on downstream tasks, such as Question answering, are based on Transformer. |
| Approach: | They propose a pre-trained monolingual Vietnamese model with three versions . they fine-tune and evaluate the model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering. |
| Outcome: | The proposed model outperforms the existing model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering. |