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pro vyhledávání: '"Araabi, Ali"'
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 <-> Hebrew. In this competition, we show that by using one model to handle bid
Externí odkaz:
http://arxiv.org/abs/2310.09946
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. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2307.12835
Neural Machine Translation (NMT) is an open vocabulary problem. As a result, dealing with the words not occurring during training (a.k.a. out-of-vocabulary (OOV) words) have long been a fundamental challenge for NMT systems. The predominant method to
Externí odkaz:
http://arxiv.org/abs/2208.05225
Autor:
Araabi, Ali, Monz, Christof
Language pairs with limited amounts of parallel data, also known as low-resource languages, remain a challenge for neural machine translation. While the Transformer model has achieved significant improvements for many language pairs and has become th
Externí odkaz:
http://arxiv.org/abs/2011.02266