2 citations · 8 across the 45 of their papers we have counts for
9 papers · 1 filter
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…
Ask Language Model to Clean Your Noisy Translation Data
Quinten Bolding, Baohao Liao, Brandon James Denis +2
Transformer models have demonstrated remarkable performance in neural machine translation (NMT). However, their vulnerability to noisy input poses a significant challenge in practi…
Multilingual k-Nearest-Neighbor Machine Translation
David Stap, Christof Monz
k-nearest-neighbor machine translation has demonstrated remarkable improvements in machine translation quality by creating a datastore of cached examples. However, these improvemen…
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…
Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables
Ali Araabi, Vlad Niculae, Christof Monz
Despite the tremendous success of Neural Machine Translation (NMT), its performance on low-resource language pairs still remains subpar, partly due to the limited ability to handle…
Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
Baohao Liao, Shaomu Tan, Christof Monz
Parameter-efficient fine-tuning (PEFT) of pre-trained language models (PLMs) has emerged as a highly successful approach, with training only a small number of parameters without sa…
Parameter-Efficient Fine-Tuning without Introducing New Latency
Baohao Liao, Yan Meng, Christof Monz
Parameter-efficient fine-tuning (PEFT) of pre-trained language models has recently demonstrated remarkable achievements, effectively matching the performance of full fine-tuning wh…
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation
Di Wu, Christof Monz
Using a vocabulary that is shared across languages is common practice in Multilingual Neural Machine Translation (MNMT). In addition to its simple design, shared tokens play an imp…
Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens
David Stap, Vlad Niculae, Christof Monz
We argue that translation quality alone is not a sufficient metric for measuring knowledge transfer in multilingual neural machine translation. To support this claim, we introduce…