Research on prompting has shown excellent performance with little or even no supervised
training across many tasks. However, prompting for machine translation is still under …

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

Recent advances of powerful Language Models have allowed Natural Language
Generation (NLG) to emerge as an important technology that can not only perform traditional …

Large language models (LLMs) have shown remarkable performance on many different
Natural Language Processing (NLP) tasks. Prompt engineering plays a key role in adding …

Large language models (LLMs) have made significant strides in various natural language
processing (NLP) tasks. Recent research shows that the moderately-sized LLMs often …

Despite the fact that document-level machine translation has inherent advantages over
sentence-level machine translation due to additional information available to a model from …

Given the great success of large language models (LLMs) across various tasks, in this
paper, we introduce LLM-ST, a novel and effective speech translation model constructed …

Despite the known limitations, most machine translation systems today still operate on the
sentence-level. One reason for this is, that most parallel training data is only sentence-level …

Directly training a document-to-document (Doc2Doc) neural machine translation (NMT) via
Transformer from scratch, especially on small datasets, usually fails to converge. Our …

Previous studies have made great advances in RST discourse parsing through specific
neural frameworks or features, but they usually split the parsing process into two subtasks …