# Iterative Domain-Repaired Back-Translation

arXiv: https://arxiv.org/abs/2010.02473 · HTML: https://mlsanity.com/paper/2010.02473/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [Adaptive Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.13022/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/2109.06604/) (2021)
- [Don't Go Far Off: An Empirical Study on Neural Poetry Translation](https://mlsanity.com/paper/2109.02972/) (2021)
- [Non-Parametric Domain Adaptation for End-to-End Speech Translation](https://mlsanity.com/paper/2205.11211/) (2022)
- [Simple and Scalable Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2302.12188/) (2023)
- [Songs Across Borders: Singable and Controllable Neural Lyric Translation](https://mlsanity.com/paper/2305.16816/) (2023)
- [Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help?](https://mlsanity.com/paper/2209.02906/) (2022)
- [Federated Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2302.12211/) (2023)
- [Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Machine Translation](https://mlsanity.com/paper/2104.05848/) (2021)
- [Learning How To Ask: Cycle-Consistency Refines Prompts in Multimodal Foundation Models](https://mlsanity.com/paper/2402.08756/) (2024)
- [BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation](https://mlsanity.com/paper/2111.06787/) (2021)
- [Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation](https://mlsanity.com/paper/2305.16599/) (2023)

## Abstract (arXiv)

In this paper, we focus on the domain-specific translation with low resources, where in-domain parallel corpora are scarce or nonexistent. One common and effective strategy for this case is exploiting in-domain monolingual data with the back-translation method. However, the synthetic parallel data is very noisy because they are generated by imperfect out-of-domain systems, resulting in the poor performance of domain adaptation. To address this issue, we propose a novel iterative domain-repaired back-translation framework, which introduces the Domain-Repair (DR) model to refine translations in synthetic bilingual data. To this end, we construct corresponding data for the DR model training by round-trip translating the monolingual sentences, and then design the unified training framework to optimize paired DR and NMT models jointly. Experiments on adapting NMT models between specific domains and from the general domain to specific domains demonstrate the effectiveness of our proposed approach, achieving 15.79 and 4.47 BLEU improvements on average over unadapted models and back-translation.