UvA-MT's Participation in the WMT23 General Translation Shared Task

no code implementations • 15 Oct 2023 • Di wu, Shaomu Tan, David Stap, Ali Araabi, Christof Monz

This paper describes the UvA-MT's submission to the WMT 2023 shared task on general machine translation.

Machine Translation Translation

Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables

no code implementations • 24 Jul 2023 • 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 previously unseen inputs, i. e., generalization.

Low-Resource Neural Machine Translation NMT +1

How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?

no code implementations • AMTA 2022 • Ali Araabi, Christof Monz, Vlad Niculae

While it is often assumed that by using BPE, NMT systems are capable of handling OOV words, the effectiveness of BPE in translating OOV words has not been explicitly measured.

Machine Translation NMT +1

Optimizing Transformer for Low-Resource Neural Machine Translation

no code implementations • COLING 2020 • Ali Araabi, Christof Monz

Language pairs with limited amounts of parallel data, also known as low-resource languages, remain a challenge for neural machine translation.

Low-Resource Neural Machine Translation Translation