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 …

Machine translation (MT) is an important task in natural language processing (NLP), as it
automates the translation process and reduces the reliance on human translators. With the …

Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant,
and fluent answers for various natural language processing (NLP) tasks. Taking document …

This paper demonstrates that multilingual denoising pre-training produces significant
performance gains across a wide variety of machine translation (MT) tasks. We present …

Neural Machine Translation (NMT) can be improved by including document-level contextual
information. For this purpose, we propose a hierarchical attention model to capture the …

Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-
art performance in a variety of translation tasks, how to use document-level context to deal …

Despite the progress made in sentence-level NMT, current systems still fall short at
achieving fluent, good quality translation for a full document. Recent works in context-aware …

The quality of automatic metrics for machine translation has been increasingly called into
question, especially for high-quality systems. This paper demonstrates that, while choice of …

Sentence simplification aims to reduce the complexity of a sentence while retaining its
original meaning. Current models for sentence simplification adopted ideas from ma-chine …

Abstract Machine translation is an important and challenging task that aims at automatically
translating natural language sentences from one language into another. Recently …