## 1 Introduction  
With the introduction of model driven architecture (MDA) by the object management group (OMG) in 2003, model-to-model (M2M) transformations have become an essential part of the modern model-driven development paradigm. An additional impulse was given by the introduction of unified modeling language (UML) 2.0, featuring a powerful model extension mechanism. With the adoption of UML 2.0, M2M transformations have become a common means of generating platform-specific models from platform-independent models, which in turn made M2M transformations a highly desirable feature in any advanced computer-aided software engineering (CASE) tool supporting MDA principles. Also, M2M transformations have become a part of common practice in other relevant fields of system engineering, such as model-based systems engineering (MBSE). Currently, M2M transformations are being extensively researched and applied in numerous system engineering contexts. One can find various approaches utilizing visual modeling environments to describe elements of source and target models and their metamodel-level relationships and mappings [1,2], or explicitly specifying transformation rules using formal language, such as QVT [3] or ATL [4]. At the same time, the natural language processing (NLP) field has been booming recently with state-of-the-art developments that were mostly influenced by breakthroughs in the field of deep learning [5,6]. They enable advanced context-driven detection of grammatical and semantic inconsistencies, extraction of relations or entities, tagging of particular subjects of interest in the text, identification of synonyms, and detection of duplicity. All of this requires fundamental knowledge of multiple techniques coming from the fields of information extraction, computational linguistics, ontology engineering, and machine learning. We argue that the application of such techniques in the context of M2M transformation has not been researched and represented enough. This could be explained by the complexity of practical implementation, as well as the lack of actual integration of advanced NLP techniques with textual and visual modeling languages. Such an observation can be made after reviewing several recent M2M transformation research papers focusing on issues related to formal specification, verification, validation, or representation rather than extension of or integration with other research domains, such as NLP [7, 8, 9, 10]. The complexity of transformation development, the steep learning curve, and the struggle to learn from examples were identified as some of the key difficulties in practical M2M transformation development and application [11]. The increasing complexity of existing transformation approaches and their implementation can be seen as yet another significant obstacle limiting their extension with additional capabilities from other domains. While model-driven development has successfully adopted so-called controlled natural language for synchronizing graphical models and their textual representations in the form of object process language [12] or tested the semantics of business vocabulary and rules (SBVR) as an alternative [13], it still does not exploit any actual NLP capabilities for M2M transformations, which require more advanced text processing of source element names to properly represent the semantics of target element names. This problem is also different from specific text-to-model transformations, which generally target predefined models and are based on text specifications, while more universal M2M transformations may be applied to different kinds of source and target models. As a consequence, the development of an M2M transformation approach that would feature high levels of extensibility, flexibility, conceptual simplicity, and usability by M2M transformation designers as well as ordinary modelers becomes a sophisticated and resource-demanding conceptual and technological issue.