V erb C raft: Morphologically-Aware A rmenian Text Generation Using LLM s in Low-Resource Settings (2025)

Abstract: Understanding 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.

## introduction armenian, an indo-european language, presents significant challenges in natural language processing (nlp) due to its intricate verb morphology. armenian verbs encode multiple layers of grammatical information, including tense, aspect, mood, voice, person, and number, using both synthetic and analytical forms @xcite . this morphological complexity leads to highly nuanced verb forms that are computationally difficult to model. morphologically rich languages (mrls) like armenian, characterized by their complex inflectional systems and scarcity of annotated data, pose unique challenges for nlp. in such languages, grammatical information is embedded within individual word forms, making accurate modeling essential for tasks such as translation and morphological analysis. despite recent advances in neural machine translation (nmt) and pretrained language models, existing approaches often fall short in handling the intricate morphological structures of mrls. standard models, such as mbart, struggle to generalize well on low-resource languages, where morphological richness compounds the difficulty of learning effective representations. to address these challenges, we introduce ver-bcraft, a morphologically aware extension of the mbart-50 model. motivated by the unique morphological complexity of armenian verbs, ver-bcraft incorporates explicit mclassifiers for predicting morphological features into the shared encoderdecoder architecture, bridging the gap between linguistic specificity and translation quality. by explicitly modeling armenian verb features, such as tense, aspect, and mood, during training, verbcraft enhances the model's capacity to generate accurate and morphologically consistent translations. a key feature of this work is the creation of a synthetic dataset using large language models (llms), such as chatgpt, coupled with human-inthe-loop validation by native armenian speakers. this strategy addresses the scarcity of annotated data for armenian, enabling the development of robust and linguistically informed nlp models. the dataset includes standard, rare, and irregular verb forms, ensuring comprehensive evaluation of the model's performance. through extensive experiments, verbcraft demonstrates significant improvements over baseline models. specifically, it achieves a bleu score of 0.4899 on the test set, compared to the baseline's 0.9975, reflecting its focus on capturing morphological precision over sentence fluency. in terms of morphological accuracy, verbcraft consistently outperforms the baseline across key features, achieving 100% accuracy in aspect predictions, 96.26% in voice, 95.33% in tense, and 91.59% in mood. these results underscore the importance of integrating linguistic supervision into nlp systems for morphologically rich languages and highlight the potential for applying this framework to other low-resource languages. this paper contributes to the field by: this paper is structured as follows: section 2 reviews related work, highlighting prior efforts in low-resource nlp, nmt, and morphological integration. section 3 describes the methodology, including model architecture, dataset creation, and evaluation setup. section 4 presents experimental results and discusses the findings, while section 5 outlines the limitations of this approach. finally, section 6 concludes the paper with insights and directions for future research. ## background and related work this section explores prior efforts in integrating morphological features into neural models, particularly for low-resource settings like armenian, and highlights their applications in neural machine translation and cross-lingual transfer. ## methodology this section describes the architecture of verbcraft, the process of dataset creation, and the evaluation setup, emphasizing the integration of explicit classifiers for predicting morphological features into the mbart-50 model and the strategies used to address data scarcity for armenian. ## results and discussion this section evaluates verbcraft on the generated dataset, analyzing its performance across various morphological features and translation accuracy. ## conclusion and future work verbcraft successfully integrates explicit morphological classifiers into the mbart-50 framework, addressing key challenges in modeling armenian verb morphology. the model achieves significant gains in: