Abstract: Machine Translation (MT) is the backbone of multiple systems and applications leveraged everyday by users. Despite research efforts and progress in the MT domain, translation remains a challenging task and MT systems struggle when translating rare words, named entities, domain-specific terminology, idiomatic expressions and culturally specific terms. To meet the translation performance expectations of users, engineers periodically update (fine-tune) MT models to guarantee translation quality. However, with ever-growing machine learning models, fine-tuning operations become more expensive, raising serious sustainability concerns. Furthermore, not all fine-tunings guarantee increased translation quality, thus wasting compute resources.

To address this issue and enhance the sustainability of MT systems, we present Flexico, an approach to engineer self-adaptive MT systems, leveraging (i) ML-based regressors to estimate the expected benefits of fine-tuning MT models; and (ii) probabilistic model checking techniques to automate the reasoning about when the benefits of fine-tuning outweigh its costs. Our empirical evaluation on two MT models and language-pairs and across 9 domains demonstrates the predictive performance of the models that estimate the expected benefits of fine-tuning and their domain-generalizability. Flexico improves the sustainability of MT systems when compared to naive baselines and decreases the number of fine-tunings while preserving high translation quality.