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.

本文提出了一种迭代式领域修复回译框架，通过域修复模型对合成双语数据进行优化，达到在特定领域适配 NMT 模型的效果提升。