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
Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
Jonathan Pilault, Xavier Garcia, Arthur Bražinskas, Orhan Firat
Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges
Andrea Piergentili, Dennis Fucci, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri
Enactive Artificial Intelligence: Subverting Gender Norms in Robot-Human Interaction
Ines Hipolito, Katie Winkle, Merete Lie
Prompting Large Language Model for Machine Translation: A Case Study
Biao Zhang, Barry Haddow, Alexandra Birch
The Recent Advances in Automatic Term Extraction: A survey
Hanh Thi Hong Tran, Matej Martinc, Jaya Caporusso, Antoine Doucet, Senja Pollak