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
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors
Marcos Treviso, Nuno M. Guerreiro, Sweta Agrawal, Ricardo Rei, José Pombal, Tania Vaz, Helena Wu, Beatriz Silva, Daan van Stigt, André F. T. Martins
Sparse Regression for Machine Translation
Ergun Biçici
A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling
Sebastian Vincent, Charlotte Prescott, Chris Bayliss, Chris Oakley, Carolina Scarton
FFN: a Fine-grained Chinese-English Financial Domain Parallel Corpus
Yuxin Fu, Shijing Si, Leyi Mai, Xi-ang Li
PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation
Christoph Leiter, Steffen Eger
Navigating the Minefield of MT Beam Search in Cascaded Streaming Speech Translation
Rastislav Rabatin, Frank Seide, Ernie Chang
ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMs
Ahmed Heakl, Youssef Zaghloul, Mennatullah Ali, Rania Hossam, Walid Gomaa
xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation Metrics
Daniil Larionov, Mikhail Seleznyov, Vasiliy Viskov, Alexander Panchenko, Steffen Eger
On the Evaluation Practices in Multilingual NLP: Can Machine Translation Offer an Alternative to Human Translations?
Rochelle Choenni, Sara Rajaee, Christof Monz, Ekaterina Shutova
Complexity of Symbolic Representation in Working Memory of Transformer Correlates with the Complexity of a Task
Alsu Sagirova, Mikhail Burtsev
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language
Shun Wang, Ge Zhang, Han Wu, Tyler Loakman, Wenhao Huang, Chenghua Lin
LLMs Are Zero-Shot Context-Aware Simultaneous Translators
Roman Koshkin, Katsuhito Sudoh, Satoshi Nakamura
Evaluating Structural Generalization in Neural Machine Translation
Ryoma Kumon, Daiki Matsuoka, Hitomi Yanaka
How effective is Multi-source pivoting for Translation of Low Resource Indian Languages?
Pranav Gaikwad, Meet Doshi, Raj Dabre, Pushpak Bhattacharyya