Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation
Computation and Language
2020-10-08 v1 Machine Learning
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.
Cite
@article{arxiv.2010.03412,
title = {Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation},
author = {Weijia Xu and Xing Niu and Marine Carpuat},
journal= {arXiv preprint arXiv:2010.03412},
year = {2020}
}
Comments
Accepted at Findings of EMNLP 2020