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    <abstract>While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared. We introduce a novel dual reconstruction objective that provides a unified view of Iterative Back-Translation and Dual Learning. It motivates a theoretical analysis and controlled empirical study on German-English and Turkish-English tasks, which both suggest that Iterative Back-Translation is more effective than Dual Learning despite its relative simplicity.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.182</identifier>
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%0 Conference Proceedings
%T Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation
%A Xu, Weijia
%A Niu, Xing
%A Carpuat, Marine
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F xu-etal-2020-dual
%X While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared. We introduce a novel dual reconstruction objective that provides a unified view of Iterative Back-Translation and Dual Learning. It motivates a theoretical analysis and controlled empirical study on German-English and Turkish-English tasks, which both suggest that Iterative Back-Translation is more effective than Dual Learning despite its relative simplicity.
%R 10.18653/v1/2020.findings-emnlp.182
%U https://aclanthology.org/2020.findings-emnlp.182
%U https://doi.org/10.18653/v1/2020.findings-emnlp.182
%P 2006-2020

Markdown (Informal)

[Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.182) (Xu et al., Findings 2020)

• Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation (Xu et al., Findings 2020)

ACL

• Weijia Xu, Xing Niu, and Marine Carpuat. 2020. Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2006–2020, Online. Association for Computational Linguistics.