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
Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstracts
Elizabeth Schaefer, Kirk Roberts
Addressing speaker gender bias in large scale speech translation systems
Shubham Bansal, Vikas Joshi, Harveen Chadha, Rupeshkumar Mehta, Jinyu Li
Finnish SQuAD: A Simple Approach to Machine Translation of Span Annotations
Emil Nuutinen, Iiro Rastas, Filip Ginter
Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)
Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona Diab
CoAM: Corpus of All-Type Multiword Expressions
Yusuke Ide, Joshua Tanner, Adam Nohejl, Jacob Hoffman, Justin Vasselli, Hidetaka Kamigaito, Taro Watanabe
The Role of Handling Attributive Nouns in Improving Chinese-To-English Machine Translation
Lisa Wang, Adam Meyers, John E. Ortega, Rodolfo Zevallos
Language verY Rare for All
Ibrahim Merad, Amos Wolf, Ziad Mazzawi, Yannick Léo
Towards Automatic Evaluation for Image Transcreation
Simran Khanuja, Vivek Iyer, Claire He, Graham Neubig
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation
Andong Chen, Yuchen Song, Kehai Chen, Muyun Yang, Tiejun Zhao, Min Zhang
Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models
Sina Bagheri Nezhad, Ameeta Agrawal, Rhitabrat Pokharel