View Translation
View translation encompasses the automated conversion of information between different modalities (e.g., text, speech, images, sign language) and languages, aiming to bridge communication gaps across diverse forms of expression. Current research emphasizes improving translation accuracy and efficiency using large language models (LLMs), exploring techniques like contrastive preference optimization, attention mechanism refinements, and multi-source pivoting, often within specific architectures such as transformers and Conformers. This field is crucial for advancing multilingual natural language processing, enabling broader access to information and facilitating cross-cultural communication in various applications, including healthcare, education, and cybersecurity.
Papers
Quantity vs. Quality of Monolingual Source Data in Automatic Text Translation: Can It Be Too Little If It Is Too Good?
Idris Abdulmumin, Bashir Shehu Galadanci, Garba Aliyu, Shamsuddeen Hassan Muhammad
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation
Kamil Kwarciak, Mateusz Daniol, Daria Hemmerling, Marek Wodzinski
Automatic Translation Alignment Pipeline for Multilingual Digital Editions of Literary Works
Maria Levchenko