The DiScEPT platform offers an innovative solution for creating digital scholarly editions with aligned translations. By integrating open-source, modular tools, it facilitates the alignment of multilingual texts, supporting comparative studies and in-depth analysis of translation processes. Adhering to FAIR principles and leveraging advanced NLP technologies for automatic text alignment.

This study presents the first systematic approach to automated interlinear translation of Ancient Greek texts using neural models. Using the New Testament as a case study, we demonstrate how machine learning can assist in creating morphologically-aware translations, achieving strong results across English and Polish target languages.

An algorithmic edition transforms digital scholarly editing by emphasizing machine-readability and computational analysis. Utilizing ATAG and ENC, it enables precise, dynamic access to text segments, annotations, and metadata. This structured, networked approach supports interdisciplinary collaboration, advancing digital humanities by integrating texts, data, and technology into comprehensive systems for scholarly exploration.