SEAMS 2025
Mon 28 - Tue 29 April 2025 Ottawa, Ontario, Canada
co-located with ICSE 2025
Tue 29 Apr 2025 14:00 - 14:25 at 204 - Session 7: Applications Chair(s): Liliana Pasquale

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 expectations of the 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) black-box predictors 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 optimizes system utility when compared to naive baselines, decreasing the number of fine-tunings required to achieve high translation quality.

Tue 29 Apr

Displayed time zone: Eastern Time (US & Canada) change

14:00 - 15:30
Session 7: ApplicationsResearch Track / Artifact Track at 204
Chair(s): Liliana Pasquale University College Dublin & Lero
14:00
25m
Talk
FLEXICO: Sustainable Machine Translation via Self-AdaptationFULL
Research Track
Maria Casimiro Instituto Superior Técnico, Universidade de Lisboa & S3D, Carnegie Mellon University, Paolo Romano IST/INESC-ID, José Sousa Unbabel, Amin M Khan INESC-ID. Universidade de Lisboa, David Garlan Carnegie Mellon University
14:25
25m
Talk
SPARQ: A QoS-aware Framework for Mitigating Cyber Risk in Self-Protecting IoT SystemsFULLBest Paper Award
Research Track
Alessandro Palma Università di Roma Sapienza, Houssam Hajj Hassan SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, Georgios Bouloukakis Télécom SudParis, Institut Polytechnique de Paris
14:50
15m
Talk
Adapting Aggregation Rule for Robust Federated Learning under Dynamic AttacksSHORT
Research Track
Chenyu Hu Southwest University, Mingyue Zhang Southwest University, NIANYU LI ZGC Lab, China, Jialong Li Waseda University, Japan, Zheng Yang Southwest University, Muneeb Ul Hassan Deakin University, Kenji Tei Institute of Science Tokyo
15:05
15m
Talk
Adaptive and Interoperable Federated Data Spaces: An Implementation ExperienceARTIFACT
Artifact Track
Nikolaos Papadakis , Niemat Khoder Télécom SudParis, Institut Polytechnique de Paris, France, Daphne Tuncer Ecole nationale des ponts et chaussees, Institut Polytechnique de Paris, France, Kostas Magoutis University of Crete and FORTH-ICS, Georgios Bouloukakis Télécom SudParis, Institut Polytechnique de Paris
15:20
10m
Other
Discussion Session 7
Research Track