Machine Translation (MT) is the backbone of a

plethora of systems and applications that are present in users’

everyday lives. Despite the 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. Thus, to meet the translation performance expec-

tations of users, engineers are tasked with periodically updating

(fine-tuning) MT models to guarantee high translation quality.

However, with ever-growing machine learning models, fine-tuning

operations become increasingly more expensive, raising serious

concerns from a sustainability perspective. Furthermore, not

all fine-tunings are guaranteed to lead to increased translation

quality, thus corresponding to wasted compute resource.

To address this issue and enhance the sustainability of MT

systems, we present FLEXICO, a new approach to engineer self-

adaptive MT systems, which leverages (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 up to 9 domains demonstrates the predictive

performance of the black-box models that estimate the expected

benefits of fine-tuning, as well as their domain-generalizability.

Finally, we show that FLEXICO improves the sustainability of

MT systems when compared to naive baselines, decreasing the

number of fine-tunings while preserving high translation quality.