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
Analyzing Context Contributions in LLM-based Machine Translation
Emmanouil Zaranis, Nuno M. Guerreiro, André F. T. Martins
On Creating an English-Thai Code-switched Machine Translation in Medical Domain
Parinthapat Pengpun, Krittamate Tiankanon, Amrest Chinkamol, Jiramet Kinchagawat, Pitchaya Chairuengjitjaras, Pasit Supholkhan, Pubordee Aussavavirojekul, Chiraphat Boonnag, Kanyakorn Veerakanjana, Hirunkul Phimsiri, Boonthicha Sae-jia, Nattawach Sataudom, Piyalitt Ittichaiwong, Peerat Limkonchotiwat
Grammatical Error Correction for Low-Resource Languages: The Case of Zarma
Mamadou K. Keita, Christopher Homan, Sofiane Abdoulaye Hamani, Adwoa Bremang, Marcos Zampieri, Habibatou Abdoulaye Alfari, Elysabhete Amadou Ibrahim, Dennis Owusu
Back to School: Translation Using Grammar Books
Jonathan Hus, Antonios Anastasopoulos
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs
Simone Conia, Daniel Lee, Min Li, Umar Farooq Minhas, Saloni Potdar, Yunyao Li
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation
Junhong Wu, Yang Zhao, Yangyifan Xu, Bing Liu, Chengqing Zong
IsoChronoMeter: A simple and effective isochronic translation evaluation metric
Nikolai Rozanov, Vikentiy Pankov, Dmitrii Mukhutdinov, Dima Vypirailenko
Code-Mixer Ya Nahi: Novel Approaches to Measuring Multilingual LLMs' Code-Mixing Capabilities
Ayushman Gupta, Akhil Bhogal, Kripabandhu Ghosh
Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs
Abdellah El Mekki, Muhammad Abdul-Mageed
QE-EBM: Using Quality Estimators as Energy Loss for Machine Translation
Gahyun Yoo, Jay Yoon Lee
ChakmaNMT: A Low-resource Machine Translation On Chakma Language
Aunabil Chakma, Aditya Chakma, Soham Khisa, Chumui Tripura, Masum Hasan, Rifat Shahriyar
Ukrainian-to-English folktale corpus: Parallel corpus creation and augmentation for machine translation in low-resource languages
Olena Burda-Lassen