Multi-Domain Adaptation in Neural Machine Translation with Dynamic Sampling Strategies

Minh-Quang Pham, Josep Crego, François Yvon


Abstract
Building effective Neural Machine Translation models often implies accommodating diverse sets of heterogeneous data so as to optimize performance for the domain(s) of interest. Such multi-source / multi-domain adaptation problems are typically approached through instance selection or reweighting strategies, based on a static assessment of the relevance of training instances with respect to the task at hand. In this paper, we study dynamic data selection strategies that are able to automatically re-evaluate the usefulness of data samples and to evolve a data selection policy in the course of training. Based on the results of multiple experiments, we show that such methods constitute a generic framework to automatically and effectively handle a variety of real-world situations, from multi-source domain adaptation to multi-domain learning and unsupervised domain adaptation.
Anthology ID:
2022.eamt-1.4
Volume:
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
Month:
June
Year:
2022
Address:
Ghent, Belgium
Editors:
Helena Moniz, Lieve Macken, Andrew Rufener, Loïc Barrault, Marta R. Costa-jussà, Christophe Declercq, Maarit Koponen, Ellie Kemp, Spyridon Pilos, Mikel L. Forcada, Carolina Scarton, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
13–22
Language:
URL:
https://aclanthology.org/2022.eamt-1.4
DOI:
Bibkey:
Cite (ACL):
Minh-Quang Pham, Josep Crego, and François Yvon. 2022. Multi-Domain Adaptation in Neural Machine Translation with Dynamic Sampling Strategies. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 13–22, Ghent, Belgium. European Association for Machine Translation.
Cite (Informal):
Multi-Domain Adaptation in Neural Machine Translation with Dynamic Sampling Strategies (Pham et al., EAMT 2022)
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PDF:
https://aclanthology.org/2022.eamt-1.4.pdf