Avetisyan, HayastanBroneske, DavidTudor, Crina MadalinaDebess, Iben NyholmBruton, MicaellaScalvini, BarbaraIlinykh, NikolaiHoldt, Špela Arhar2025-02-142025-02-142025-03https://aclanthology.org/2025.resourceful-1.0/https://hdl.handle.net/10062/107123Understanding and generating morphologically complex verb forms is a critical challenge in Natural Language Processing (NLP), particularly for low-resource languages like Armenian. Armenian's verb morphology encodes multiple layers of grammatical information, such as tense, aspect, mood, voice, person, and number, requiring nuanced computational modeling. We introduce VerbCraft, a novel neural model that integrates explicit morphological classifiers into the mBART-50 architecture. VerbCraft achieves a BLEU score of 0.4899 on test data, compared to the baseline's 0.9975, reflecting its focus on prioritizing morphological precision over fluency. With over 99\% accuracy in aspect and voice predictions and robust performance on rare and irregular verb forms, VerbCraft addresses data scarcity through synthetic data generation with human-in-the-loop validation. Beyond Armenian, it offers a scalable framework for morphologically rich, low-resource languages, paving the way for linguistically informed NLP systems and advancing language preservation efforts.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/Article