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
Evaluation of Chinese-English Machine Translation of Emotion-Loaded Microblog Texts: A Human Annotated Dataset for the Quality Assessment of Emotion Translation
Shenbin Qian, Constantin Orasan, Felix do Carmo, Qiuliang Li, Diptesh Kanojia
Efficient Machine Translation Corpus Generation
Kamer Ali Yuksel, Ahmet Gunduz, Shreyas Sharma, Hassan Sawaf
EvolveMT: an Ensemble MT Engine Improving Itself with Usage Only
Kamer Ali Yuksel, Ahmet Gunduz, Mohamed Al-Badrashiny, Shreyas Sharma, Hassan Sawaf