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.- Anthology ID:
- 2020.findings-emnlp.182
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2006–2020
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.182
- DOI:
- 10.18653/v1/2020.findings-emnlp.182
- Cite (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.
- Cite (Informal):
- Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation (Xu et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.182.pdf