1 code implementation • Findings (ACL) 2022 • Thuy-Trang Vu, Shahram Khadivi, Dinh Phung, Gholamreza Haffari
Generalising to unseen domains is under-explored and remains a challenge in neural machine translation.
no code implementations • 20 Oct 2022 • Thuy-Trang Vu, Shahram Khadivi, Xuanli He, Dinh Phung, Gholamreza Haffari
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT).
1 code implementation • EMNLP 2021 • Thuy-Trang Vu, Xuanli He, Dinh Phung, Gholamreza Haffari
Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks.
1 code implementation • EMNLP 2020 • Thuy-Trang Vu, Dinh Phung, Gholamreza Haffari
Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest.
1 code implementation • ACL 2019 • Thuy-Trang Vu, Ming Liu, Dinh Phung, Gholamreza Haffari
Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.
1 code implementation • EMNLP 2018 • Thuy-Trang Vu, Gholamreza Haffari
Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output.