Scaling neural machine translation to 200 languages. [PDF]
NatureThe development of neural techniques has opened up new avenues for research in machine translation. Today, neural machine translation (NMT) systems can leverage highly multilingual capacities and even perform zero-shot translation, delivering promising ...
NLLB Team.
europepmc +2 more sources
The use of automation in the rendition of certain articles of the Saudi Commercial Law into English: a post-editing-based comparison of five machine translation systems [PDF]
Frontiers in Artificial Intelligence, 2023 Efforts to automate translation were made in the 1950s and 1960s, albeit with limited resources compared to current advanced standards. Machine translation is categorised under computational linguistics that examines employing computer software in the ...
Rafat Y. Alwazna
doaj +2 more sources
No Language Left Behind: Scaling Human-Centered Machine Translation [PDF]
arXiv.org, 2022 Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today.
Nllb team +38 more
semanticscholar +1 more source
How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation [PDF]
arXiv.org, 2023 Generative Pre-trained Transformer (GPT) models have shown remarkable capabilities for natural language generation, but their performance for machine translation has not been thoroughly investigated.
Amr Hendy +8 more
semanticscholar +1 more source
xcomet: Transparent Machine Translation Evaluation through Fine-grained Error Detection [PDF]
Transactions of the Association for Computational Linguistics, 2023 Widely used learned metrics for machine translation evaluation, such as Comet and Bleurt, estimate the quality of a translation hypothesis by providing a single sentence-level score.
Nuno M. Guerreiro +5 more
semanticscholar +1 more source
Automatic evaluation of the quality of machine translation of a scientific text: the results of a five-year-long experiment [PDF]
E3S Web of Conferences, 2021 We report on various approaches to automatic evaluation of machine translation quality and describe three widely used methods. These methods, i.e. methods based on string matching and n-gram models, make it possible to compare the quality of machine ...
Ulitkin Ilya +3 more
doaj +1 more source
Prompting Large Language Model for Machine Translation: A Case Study [PDF]
International Conference on Machine Learning, 2023 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-explored in the literature.
Biao Zhang, B. Haddow, Alexandra Birch
semanticscholar +1 more source
Survey of Mongolian-Chinese Neural Machine Translation [PDF]
Jisuanji kexue, 2022 Machine translation is the process of using a computer to convert one language into another language.With the deep understanding of semantics,neural machine translation has become the most mainstream machine translation method at present,and it has made ...
HOU Hong-xu, SUN Shuo, WU Nier
doaj +1 more source
Towards Making the Most of ChatGPT for Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resource and distant ...
Keqin Peng +7 more
semanticscholar +1 more source
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis [PDF]
NAACL-HLT, 2023 Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT). In this paper, we systematically investigate the advantages and challenges of LLMs for MMT by answering two questions: 1) How well do ...
Wenhao Zhu +7 more
semanticscholar +1 more source