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
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
How Multilingual Are Large Language Models Fine-Tuned for Translation?
Aquia Richburg, Marine Carpuat
The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
David Stap, Eva Hasler, Bill Byrne, Christof Monz, Ke Tran
Significance of Chain of Thought in Gender Bias Mitigation for English-Dravidian Machine Translation
Lavanya Prahallad, Radhika Mamidi
Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth
Zhaoyang Sun, Shengwu Xiong, Yaxiong Chen, Yi Rong
The Multi-Range Theory of Translation Quality Measurement: MQM scoring models and Statistical Quality Control
Arle Lommel, Serge Gladkoff, Alan Melby, Sue Ellen Wright, Ingemar Strandvik, Katerina Gasova, Angelika Vaasa, Andy Benzo, Romina Marazzato Sparano, Monica Foresi, Johani Innis, Lifeng Han, Goran Nenadic