Transductive data-selection algorithms for fine-tuning neural machine translation
Poncelas, AlbertoORCID: 0000-0002-5089-1687, Maillette de Buy Wenniger, GideonORCID: 0000-0001-8427-7055 and Way, AndyORCID: 0000-0001-5736-5930
(2019)
Transductive data-selection algorithms for fine-tuning neural machine translation.
In: The 8th Workshop on Patent and Scientific Literature Translation, Dublin, Ireland.
Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a technique for adapting an NMT model to some domain. In this work, we want to use this technique to adapt the model to a given test set. In particular, we are using transductive data selection algorithms which take advantage the information of the test set to retrieve sentences from a larger parallel set.
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Funders:
SFI Research Centres Programme (Grant 13/RC/2106), European Regional Development Fund, European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 713567.
ID Code:
24061
Deposited On:
02 Jan 2020 09:52 by
Alberto Poncelas
. Last Modified 22 Jan 2021 14:23