1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance
Shaomu Tan, Christof Monz
Multilingual Neural Machine Translation (MNMT) facilitates knowledge sharing but often suffers from poor zero-shot (ZS) translation qualities. While prior work has explored the cau…
UvA-MT's Participation in the WMT23 General Translation Shared Task
Di Wu, Shaomu Tan, David Stap +2
This paper describes the UvA-MT's submission to the WMT 2023 shared task on general machine translation. We participate in the constrained track in two directions: English <-> Hebr…
Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout Analysis
Sotirios Kastanas, Shaomu Tan, Yi He
Document AI aims to automatically analyze documents by leveraging natural language processing and computer vision techniques. One of the major tasks of Document AI is document layo…