The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

$ k $ NN-MT is a straightforward yet powerful approach for fast domain adaptation, which
directly plugs pre-trained neural machine translation (NMT) models with domain-specific …

Recently, $ k $ NN-MT has shown the promising capability of directly incorporating the pre-
trained neural machine translation (NMT) model with domain-specific token-level $ k …

Purpose Social media's role in engaging participants in sports events, particularly during the
pandemic, is acknowledged. However, previous studies often utilized sports events for …

To protect user privacy and meet legal regulations, federated learning (FL) is attracting
significant attention. Training neural machine translation (NMT) models with traditional FL …

End-to-End Speech Translation (E2E-ST) has received increasing attention due to the
potential of its less error propagation, lower latency, and fewer parameters. However, the …

Neural Machine Translation (NMT) brings promising improvements in translation quality, but
until recently, these models rely on large-scale parallel corpora. As such corpora only exist …

$ k $-Nearest neighbor machine translation ($ k $ NN-MT) has attracted increasing attention
due to its ability to non-parametrically adapt to new translation domains. By using an …

The development of general-domain neural machine translation (NMT) methods has
advanced significantly in recent years, but the lack of naturalness and musical constraints in …

Despite constant improvements in machine translation quality, automatic poetry translation
remains a challenging problem due to the lack of open-sourced parallel poetic corpora, and …