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
Textual Similarity as a Key Metric in Machine Translation Quality Estimation
Kun Sun, Rong Wang
CTC-based Non-autoregressive Textless Speech-to-Speech Translation
Qingkai Fang, Zhengrui Ma, Yan Zhou, Min Zhang, Yang Feng
DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms
Andong Chen, Lianzhang Lou, Kehai Chen, Xuefeng Bai, Yang Xiang, Muyun Yang, Tiejun Zhao, Min Zhang
Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning
Menglong Cui, Jiangcun Du, Shaolin Zhu, Deyi Xiong
Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation
Nathaniel Berger, Stefan Riezler, Miriam Exel, Matthias Huck
OTTAWA: Optimal TransporT Adaptive Word Aligner for Hallucination and Omission Translation Errors Detection
Chenyang Huang, Abbas Ghaddar, Ivan Kobyzev, Mehdi Rezagholizadeh, Osmar R. Zaiane, Boxing Chen