This paper illustrates a workflow for developing and evaluating automatic translation alignment models for Ancient Greek. We designed an annotation Style Guide and a gold standard for the alignment of Ancient Greek-English and Ancient Greek-Portuguese, measured inter-annotator agreement and used the resulting dataset to evaluate the performance of various translation alignment models. We proposed a fine-tuning strategy that employs unsupervised training with mono- and bilingual texts and supervised training using manually aligned sentences. The results indicate that the fine-tuned model based on XLM-Roberta is superior in performance, and it achieved good results on language pairs that were not part of the training data.

Text alignment is one of the fundamental techniques text-related domains like natural language processing, computational linguistics, and digital humanities. It compares two or more texts with each other aiming to find similar textual patterns, or to estimate in general how different or similar the texts are. Visualizing alignment results is an essential task, because it helps researchers getting a comprehensive overview of individual findings and the overall pattern structure. Different approaches have been developed to visualize and help making sense of these patterns depending on text size, alignment methods, and, most importantly, the underlying research tasks demanding for alignment. On the basis of those tasks, we reviewed existing text alignment visualization approaches, and discuss their advantages and drawbacks. We finally derive design implications and shed light on related future challenges.

The Ancient Greek WordNet (AGWN) and the Dynamic Lexicon (DL) are multilingual resources to study the lexicon of Ancient Greek texts and their translations.Both AGWN and DL are works in progress that need accuracy improvement and manual validation.After a detailed description of the current state of each work, this paper illustrates a methodology to cross AGWN and DL data, in order to mutually score the items of each resource according to the evidence provided by the other resource.The training data is based on the corpus of the Digital Fragmenta Historicorum Graecorum (DFHG), which includes ancient Greek texts with Latin translations.