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
Evaluating Machine Translation Quality with Conformal Predictive Distributions
Patrizio Giovannotti
Text Style Transfer Back-Translation
Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
Automatic Translation of Hate Speech to Non-hate Speech in Social Media Texts
Yevhen Kostiuk, Atnafu Lambebo Tonja, Grigori Sidorov, Olga Kolesnikova
Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation
Josef Jon, Ondřej Bojar
Translation-Enhanced Multilingual Text-to-Image Generation
Yaoyiran Li, Ching-Yun Chang, Stephen Rawls, Ivan Vulić, Anna Korhonen
BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation
Taisiya Glushkova, Chrysoula Zerva, André F. T. Martins