Dynamic adaptation of neural machine-translation systems through translation exemplars
- Author
- Arda Tezcan (UGent)
- Organization
- Abstract
- This project aims to study the impact of adapting neural machine translation (NMT) systems through translation exemplars, determine the optimal similarity metric(s) for retrieving informative exemplars, and, verify the usefulness of this approach for domain adaptation of NMT systems.
- Keywords
- neural machine translation, translation memories, lt3
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8761020
- MLA
- Tezcan, Arda. “Dynamic Adaptation of Neural Machine-Translation Systems through Translation Exemplars.” Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, edited by Lieve Macken et al., European Association for Machine Translation, 2022, pp. 283–84.
- APA
- Tezcan, A. (2022). Dynamic adaptation of neural machine-translation systems through translation exemplars. In L. Macken, A. Rufener, J. Van den Bogaert, J. Daems, A. Tezcan, B. Vanroy, … H. Moniz (Eds.), Proceedings of the 23rd Annual Conference of the European Association for Machine Translation (pp. 283–284). Ghent, Belgium: European Association for Machine Translation.
- Chicago author-date
- Tezcan, Arda. 2022. “Dynamic Adaptation of Neural Machine-Translation Systems through Translation Exemplars.” In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, edited by Lieve Macken, Andrew Rufener, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne, et al., 283–84. Ghent, Belgium: European Association for Machine Translation.
- Chicago author-date (all authors)
- Tezcan, Arda. 2022. “Dynamic Adaptation of Neural Machine-Translation Systems through Translation Exemplars.” In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, ed by. Lieve Macken, Andrew Rufener, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne, Loïc Barrault, Marta R. Costa-jussà, Ellie Kemp, Spyridon Pilos, Christophe Declercq, Maarit Koponen, Mikel L. Forcada, Carolina Scarton, and Helena Moniz, 283–284. Ghent, Belgium: European Association for Machine Translation.
- Vancouver
- 1.Tezcan A. Dynamic adaptation of neural machine-translation systems through translation exemplars. In: Macken L, Rufener A, Van den Bogaert J, Daems J, Tezcan A, Vanroy B, et al., editors. Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. Ghent, Belgium: European Association for Machine Translation; 2022. p. 283–4.
- IEEE
- [1]A. Tezcan, “Dynamic adaptation of neural machine-translation systems through translation exemplars,” in Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, Ghent, Belgium, 2022, pp. 283–284.
@inproceedings{8761020,
abstract = {{This project aims to study the impact of adapting neural machine translation (NMT) systems through translation exemplars, determine the optimal similarity metric(s) for retrieving informative exemplars, and, verify the usefulness of this approach for domain adaptation of NMT systems.}},
author = {{Tezcan, Arda}},
booktitle = {{Proceedings of the 23rd Annual Conference of the European Association for Machine Translation}},
editor = {{Macken, Lieve and Rufener, Andrew and Van den Bogaert, Joachim and Daems, Joke and Tezcan, Arda and Vanroy, Bram and Fonteyne, Margot and Barrault, Loïc and Costa-jussà, Marta R. and Kemp, Ellie and Pilos, Spyridon and Declercq, Christophe and Koponen, Maarit and Forcada, Mikel L. and Scarton, Carolina and Moniz, Helena}},
isbn = {{9789464597622}},
keywords = {{neural machine translation,translation memories,lt3}},
language = {{eng}},
location = {{Ghent, Belgium}},
pages = {{283--284}},
publisher = {{European Association for Machine Translation}},
title = {{Dynamic adaptation of neural machine-translation systems through translation exemplars}},
url = {{https://aclanthology.org/2022.eamt-1.31}},
year = {{2022}},
}