Machine Translation
Machine translation (MT) aims to automatically translate text between languages, with current research heavily focused on leveraging large language models (LLMs) and exploring various architectures like encoder-decoder and decoder-only models. Key areas of investigation include improving translation quality, particularly for low-resource languages and specialized domains like medicine, mitigating biases (e.g., gender bias), and enhancing evaluation methods beyond simple correlation with human judgments. These advancements have significant implications for cross-cultural communication, information access, and the development of more equitable and effective multilingual technologies.
Papers
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors
Marcos Treviso, Nuno M. Guerreiro, Sweta Agrawal, Ricardo Rei, José Pombal, Tania Vaz, Helena Wu, Beatriz Silva, Daan van Stigt, André F. T. Martins
Sparse Regression for Machine Translation
Ergun Biçici
A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling
Sebastian Vincent, Charlotte Prescott, Chris Bayliss, Chris Oakley, Carolina Scarton
FFN: a Fine-grained Chinese-English Financial Domain Parallel Corpus
Yuxin Fu, Shijing Si, Leyi Mai, Xi-ang Li