Continual learning (CL) aims to enable information systems to learn from a continuous data
stream across time. However, it is difficult for existing deep learning architectures to learn a …

The rapid development of Large Language Models (LLMs) demonstrates remarkable
multilingual capabilities in natural language processing, attracting global attention in both …

While task-specific finetuning of pretrained networks has led to significant empirical
advances in NLP, the large size of networks makes finetuning difficult to deploy in multi-task …

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 …

Deep pretrained language models have achieved great success in the way of pretraining
first and then fine-tuning. But such a sequential transfer learning paradigm often confronts …

In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially
outperforms training from scratch. When using fine-tuning, the underlying assumption is that …

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

Considerable effort has been made to address the data sparsity problem in neural
grammatical error correction. In this work, we propose a simple and surprisingly effective …

The dependency of the generalization error of neural networks on model and dataset size is
of critical importance both in practice and for understanding the theory of neural networks …

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