Feature Decay Algorithms for Neural Machine Translation

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/76084
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Title: Feature Decay Algorithms for Neural Machine Translation
Authors: Poncelas, Alberto | Maillette de Buy Wenniger, Gideon | Way, Andy
Keywords: Machine Translation
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 2018
Publisher: European Association for Machine Translation
Citation: Poncelas, Alberto; Maillette de Buy Wenniger, Gideon; Way, Andy. “Feature Decay Algorithms for Neural Machine Translation”. In: Pérez-Ortiz, Juan Antonio, et al. (Eds.). Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain, pp. 239-248
Abstract: Neural Machine Translation (NMT) systems require a lot of data to be competitive. For this reason, data selection techniques are used only for fine-tuning systems that have been trained with larger amounts of data. In this work we aim to use Feature Decay Algorithms (FDA) data selection techniques not only to fine-tune a system but also to build a complete system with less data. Our findings reveal that it is possible to find a subset of sentence pairs, that outperforms by 1.11 BLEU points the full training corpus, when used for training a German-English NMT system.
Sponsor: This research has been supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund. This work has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 713567.
URI: http://hdl.handle.net/10045/76084
ISBN: 978-84-09-01901-4
Language: eng
Type: info:eu-repo/semantics/conferenceObject
Rights: © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.
Peer Review: si
Publisher version: http://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdf
Appears in Collections:Research funded by the EU
Congresos - EAMT2018 - Proceedings

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