Search Results for author:
Found 7 papers, 4 papers with code
Neuron Specialization: Leveraging intrinsic task modularity for multilingual machine translation
Training a unified multilingual model promotes knowledge transfer but inevitably introduces negative interference.
How Far Can 100 Samples Go? Unlocking Overall Zero-Shot Multilingual Translation via Tiny Multi-Parallel Data
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.
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance
Our findings highlight that the target side translation quality is the most influential factor, with vocabulary overlap consistently impacting ZS performance.
UvA-MT's Participation in the WMT23 General Translation Shared Task
This paper describes the UvA-MT's submission to the WMT 2023 shared task on general machine translation.
Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout Analysis
In this study, we aim to fill these gaps by conducting a comparative evaluation of state-of-the-art models in document layout analysis and investigating the potential of cross-lingual layout analysis by utilizing machine translation techniques.
Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
One effective way to reduce the activation memory is to apply a reversible model, so the intermediate activations are not necessary to be cached and can be recomputed.
Towards leveraging latent knowledge and Dialogue context for real-world conversational question answering
In many real-world scenarios, the absence of external knowledge source like Wikipedia restricts question answering systems to rely on latent internal knowledge in limited dialogue data.
