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
SpeechQE: Estimating the Quality of Direct Speech Translation
HyoJung Han, Kevin Duh, Marine Carpuat
Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review
Catherine Ikae, Mascha Kurpicz-Briki
CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models
Meiqi Chen, Fandong Meng, Yingxue Zhang, Yan Zhang, Jie Zhou
Instruction-Tuned LLMs Succeed in Document-Level MT Without Fine-Tuning -- But BLEU Turns a Blind Eye
Yirong Sun, Dawei Zhu, Yanjun Chen, Erjia Xiao, Xinghao Chen, Xiaoyu Shen