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
Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus
Andrea Piergentili, Beatrice Savoldi, Dennis Fucci, Matteo Negri, Luisa Bentivogli
Synslator: An Interactive Machine Translation Tool with Online Learning
Jiayi Wang, Ke Wang, Fengming Zhou, Chengyu Wang, Zhiyong Fu, Zeyu Feng, Yu Zhao, Yuqi Zhang
Cross-Modal Multi-Tasking for Speech-to-Text Translation via Hard Parameter Sharing
Brian Yan, Xuankai Chang, Antonios Anastasopoulos, Yuya Fujita, Shinji Watanabe
Enhancing End-to-End Conversational Speech Translation Through Target Language Context Utilization
Amir Hussein, Brian Yan, Antonios Anastasopoulos, Shinji Watanabe, Sanjeev Khudanpur
Developing automatic verbatim transcripts for international multilingual meetings: an end-to-end solution
Akshat Dewan, Michal Ziemski, Henri Meylan, Lorenzo Concina, Bruno Pouliquen
Exploring the Impact of Training Data Distribution and Subword Tokenization on Gender Bias in Machine Translation
Bar Iluz, Tomasz Limisiewicz, Gabriel Stanovsky, David Mareček
OSN-MDAD: Machine Translation Dataset for Arabic Multi-Dialectal Conversations on Online Social Media
Fatimah Alzamzami, Abdulmotaleb El Saddik
A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models
Haoran Xu, Young Jin Kim, Amr Sharaf, Hany Hassan Awadalla
Towards Effective Disambiguation for Machine Translation with Large Language Models
Vivek Iyer, Pinzhen Chen, Alexandra Birch
SignBank+: Preparing a Multilingual Sign Language Dataset for Machine Translation Using Large Language Models
Amit Moryossef, Zifan Jiang