TS.Trần Văn Khánh

Faculty Profile
Điện thoại 0838672666
Chức vụ Phó Viện trưởng
Địa chỉ

Phó Viện Trưởng Viện Khoa Học và Công Nghệ Ứng Dụng

Tòa nhà C6

Trường Đại học Công nghệ Thông tin và Truyền thông

Kỹ năng và chuyên môn

  • Natural Language Generation
  • Dialogue
  • Deep Learning
  • Natural Language Processing
  • Adaptive Spoken Dialogue System
  • Machine Learning
  • Semantic Refinement GRU-based Neural Language Generation for Spoken Dialogue Systems (2017)
  • VBD-MT Chinese-Vietnamese Translation Systems for VLSP 2022 (2023)
  • Variational Model for Low-Resource Natural Language Generation in Spoken Dialogue Systems (2020)
  • Dual Latent Variable Model for Low-Resource Natural Language Generation in Dialogue Systems (2018)
  • Gating Mechanism based Natural Language Generation for Spoken Dialogue Systems (2018)
  • Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems (2018)
  • Dual Latent Variable Model for Low-Resource Natural Language Generation in Dialogue Systems (2018)
  • Semantic Refinement GRU-Based Neural Language Generation for Spoken Dialogue Systems (2018)
  • Towards domain adaptation for Neural Network Language Generation in Dialogue (2017)
  • Enhanced Semantic Refinement Gate for RNN-based Neural Language Generator (2017)
  • Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks (2017)
  • Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation (2017)
  • Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities (2017)
  • Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks (2017)
  • Lexical to Discourse-level Corpus Modeling for Legal Question Answering (2016)

Danh mục các công bố

2024 [ to top ]

Van-Khanh Tran, Thai-Hoc Nguyen, Xuan-Lam Dinh, Chi-Cuong Nghiem, "Study on Ensemble Learning for Cervical Cytology Classification", In International Conference on Advances in Information and Communication Technology (ICTA 2024), Phu Tho, Vietnam, November 2024.

Van-Khanh Tran, Duy-Thanh Do, Xuan-Lam Dinh, Chi-Cuong Nghiem, "Transfer Learning for Cervical Cancer Multi-Class Classification". In International Conference on Advances in Information and Communication Technology (ICTA 2024), Phu Tho, Vietnam, November 2024

Van-Hieu Nguyen, La, Dinh-Dien, Trung-Nghia Phung & Tran Khanh-Van. (2024). Analysis of Document Retrieval for Online Public Administrative Procedure Services. Research and Development on Information and Communication Technology. 4. 10.32913/mic-ict-research-vn.v2024.n2.1297. 

Dinh-Dien La, Tuan-Anh Nguyen, Duc-Huy Mai, Thi-Thanh Ha, Trung-Nghia Phung, and Van-Khanh Tran, "A Large Language Model-Based Question Answering System for Online Public Administrative Services", In International Conference on Advances in Information and Communication Technology (ICTA 2024), Phu Tho, Vietnam, November 2024.

2023 [ to top ]

Trieu, Hai Long, Song Kiet Bui, Tan Minh Tran, Van Khanh Tran, and Hai An Nguyen. "VBD-MT Chinese-Vietnamese Translation Systems for VLSP 2022.arXiv preprint arXiv:2308.07601 (2023).

2021 [ to top ]

Tran, Van-Khanh, and Le-Minh Nguyen. "Variational model for low-resource natural language generation in spoken dialogue systems." Computer Speech & Language 65 (2021): 101120.

2018 [ to top ]

Tran, Van-Khanh, and Le-Minh Nguyen. "Dual latent variable model for low-resource natural language generation in dialogue systems." arXiv preprint arXiv:1811.04164 (2018).

Tran, Van-Khanh, and Le-Minh Nguyen. "Gating mechanism based natural language generation for spoken dialogue systems." Neurocomputing 325 (2019): 48-58.

Tran, Van-Khanh, and Le-Minh Nguyen. "Adversarial domain adaptation for variational neural language generation in dialogue systems." arXiv preprint arXiv:1808.02586 (2018).

Tran, Van-Khanh, and Le-Minh Nguyen. "Dual latent variable model for low-resource natural language generation in dialogue systems." arXiv preprint arXiv:1811.04164 (2018).

Tran, Van-Khanh, and Le-Minh Nguyen. "Semantic refinement gru-based neural language generation for spoken dialogue systems." Computational Linguistics: 15th International Conference of the Pacific Association for Computational Linguistics, PACLING 2017, Yangon, Myanmar, August 16–18, 2017, Revised Selected Papers 15. Springer Singapore, 2018.

2017 [ to top ]

Tran, Van-Khanh, et al. "Towards domain adaptation for neural network language generation in dialogue." 2017 4th NAFOSTED Conference on Information and Computer Science. IEEE, 2017.

Tran, Van-Khanh, Van-Tao Nguyen, and Le-Minh Nguyen. "Enhanced semantic refinement gate for RNN-based neural language generator." 2017 9th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2017.

Tran, Van-Khanh, and Le-Minh Nguyen. "Natural language generation for spoken dialogue system using rnn encoder-decoder networks." arXiv preprint arXiv:1706.00139 (2017).

Tran, Van-Khanh, and Le-Minh Nguyen. "Neural-based natural language generation in dialogue using rnn encoder-decoder with semantic aggregation." arXiv preprint arXiv:1706.06714 (2017).

Carvalho, D. S., Tran, V. D., Tran, V. K., & Nguyen, L. M. (2017, June). Improving legal information retrieval by distributional composition with term order probabilities. In COLIEE@ ICAIL (pp. 43-56).

Tran, Van-Khanh, and Le-Minh Nguyen. "Natural language generation for spoken dialogue system using rnn encoder-decoder networks." arXiv preprint arXiv:1706.00139 (2017).

Tran, Van-Khanh, and Le-Minh Nguyen. "Semantic refinement gru-based neural language generation for spoken dialogue systems." Computational Linguistics: 15th International Conference of the Pacific Association for Computational Linguistics, PACLING 2017, Yangon, Myanmar, August 16–18, 2017, Revised Selected Papers 15. Springer Singapore, 2018.

2016 [ to top ]

Carvalho, D. S., Tran, V. D., Van Tran, K., Lai, V. D., & Nguyen, M. L. (2016). Lexical to discourse-level corpus modeling for legal question answering. In Tenth International Workshop on Juris-Informatics (JURISIN).