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
FLEURS-ASL: Including American Sign Language in Massively Multilingual Multitask Evaluation
Garrett Tanzer
Cultural Adaptation of Menus: A Fine-Grained Approach
Zhonghe Zhang, Xiaoyu He, Vivek Iyer, Alexandra Birch
Generative-Adversarial Networks for Low-Resource Language Data Augmentation in Machine Translation
Linda Zeng
Generating Gender Alternatives in Machine Translation
Sarthak Garg, Mozhdeh Gheini, Clara Emmanuel, Tatiana Likhomanenko, Qin Gao, Matthias Paulik
An Energy-based Model for Word-level AutoCompletion in Computer-aided Translation
Cheng Yang, Guoping Huang, Mo Yu, Zhirui Zhang, Siheng Li, Mingming Yang, Shuming Shi, Yujiu Yang, Lemao Liu
Teaching LLMs at Charles University: Assignments and Activities
Jindřich Helcl, Zdeněk Kasner, Ondřej Dušek, Tomasz Limisiewicz, Dominik Macháček, Tomáš Musil, Jindřich Libovický