1 citations · 1 across the 8 of their papers we have counts for
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Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?
Seth Aycock, David Stap, Di Wu +2
Extremely low-resource (XLR) languages lack substantial corpora for training NLP models, motivating the use of all available resources such as dictionaries and grammar books. Machi…
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…
Neuron Specialization: Leveraging intrinsic task modularity for multilingual machine translation
Shaomu Tan, Di Wu, Christof Monz
Training a unified multilingual model promotes knowledge transfer but inevitably introduces negative interference. Language-specific modeling methods show promise in reducing inter…
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…