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cs.CL2024
How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation
Yan Meng, Di Wu, Christof Monz
The massive amounts of web-mined parallel data contain large amounts of noise. Semantic misalignment, as the primary source of the noise, poses a challenge for training machine tra…
cs.CL2024
How Far Can 100 Samples Go? Unlocking Overall Zero-Shot Multilingual Translation via Tiny Multi-Parallel Data
Di Wu, Shaomu Tan, Yan Meng +2
Zero-shot translation aims to translate between language pairs not seen during training in Multilingual Machine Translation (MMT) and is largely considered an open problem. A commo…
cs.CL2024
Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation
Yan Meng, Christof Monz
Multilingual Machine Translation (MMT) benefits from knowledge transfer across different language pairs. However, improvements in one-to-many translation compared to many-to-one tr…