Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach
for adapting to new languages and domains. However, fine-tuning requires adapting and …

Neural machine translation (NMT) is a deep learning based approach for machine
translation, which outperforms traditional statistical machine translation (SMT) and yields the …

Recent work in Neural Machine Translation (NMT) has shown significant quality gains from
noised-beam decoding during back-translation, a method to generate synthetic parallel …

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

Continued training is an effective method for domain adaptation in neural machine
translation. However, in-domain gains from adaptation come at the expense of general …

Neural machine translation (NMT) has proven to be facilitated by curriculum learning which
presents examples in an easy-to-hard order at different training stages. The keys lie in the …

The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in
morphology examined transfer learning of inflection between 100 language pairs, as well as …

Software vulnerabilities are now reported unprecedentedly due to the recent development of
automated vulnerability hunting tools. However, fixing vulnerabilities still mainly depends on …

Large language models (LLMs) have shown promising performance for various downstream
tasks, especially machine translation. However, LLMs and Specialized Translation Models …