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
VALHALLA: Visual Hallucination for Machine Translation
Yi Li, Rameswar Panda, Yoon Kim, Chun-Fu Chen, Rogerio Feris, David Cox, Nuno Vasconcelos
Preparing an Endangered Language for the Digital Age: The Case of Judeo-Spanish
Alp Öktem, Rodolfo Zevallos, Yasmin Moslem, Güneş Öztürk, Karen Şarhon
Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Model
Xuan-Phi Nguyen, Shafiq Joty, Wu Kui, Ai Ti Aw