Classical Philology in the Time of AI : Exploring the Potential of Parallel Corpora in Ancient Language (2023)

Abstract: This paper provides an overview of diverse applications of parallel corpora in ancient languages, particularly Ancient Greek. In the first part, we provide the fundamental principles of parallel corpora and a short overview of their applications in the study of ancient texts. In the second part, we illustrate how to leverage on parallel corpora to perform various NLP tasks, including automatic translation alignment, dynamic lexica induction, and Named Entity Recognition. In the conclusions, we emphasize current limitations and future work.

## introduction parallel text processing refers to various computational tasks based on parallel corpora @xcite . parallel corpora are collections of texts that show some level of equivalence between them: for example, a text and its translations, or different versions of the same text. the most important task in parallel text processing is text alignment, that is, the automatic establishment of equivalences across various types of units: document, chunk, sentence, and word @xcite . the task of aligning a text against its translation(s) is called text-translation alignment (from now on, ta). the output of ta is defined as translation pairs (tps), which correspond to pairs of the various units aligned (chunks, sentences, words, etc.). ta can be considered a subfield of text alignment: however, it has very unique challenges attributed to the complex dynamics underlying the relationship between texts and their translations. in particular, word-level ta poses considerable complexity due to the inherent uncertainty in establishing individual equivalences: translations are not perfect transpositions of the originals, and tend to alter, normalize, expand or simplify parts of the text. moreover, structural differences across languages, such as morphology and word order, contribute to additional difficulties. the goal of this paper is to offer a programmatic survey of the current status of ta research in the specific domain of ancient languages, particularly ancient greek. as such, we will cover many different applications, both in philology and computer science, with the intent of demonstrating the potential of this method in the study of ancient languages. our aim is to illustrate how ta and parallel corpora can be used for a wide range of research, to contribute to existing debates and to inspire new questions. ## design and concept of translation alignment tools since ta was established, several tools have been designed to collect tps, with or without integrated reading environments for visualizing the alignments (overviews are provided in our previous works @xcite . in the context of ancient languages, a limited number of tools have been developed. these include alpheios @xcite , ducat citation alignment tool @xcite ), benner's tool for aligning the bible @xcite , and ugarit foot_0 , designed to enable word-level alignments in low-resourced languages @xcite . currently, ugarit counts about 50 aligned languages, 700 users, and more than a million tps 2 , establishing itself as the most popular tool in this area. ugarit was designed as a crowd-sourcing project to collect training data for automatic alignment methods for ancient languages, but it expanded into a range of diverse applications, mostly thanks to its global community of scholars and students. the alignment workflow, which allows bilingual and trilingual alignments, is simple and intuitive.ugarit allows different types of tps: word-to-word (1-1), word-to-phrase (1-n), phraseto-word (n-1) and phrase-to-phrase (n-n). alignments are immediately published online. the visualization of published alignments allows the user to compare aligned texts token by token, providing a transliteration service for non-latin alphabets, statistical information about the percentage of aligned and not-aligned tokens, types of links, a downloadable list of tps, and an embedding option @xcite . the tool integrates a dynamic lexicon, which can be triggered through the search function or simply by clicking on a word in an aligned text. the results are visualized as a radial cluster dendrogram, a tree view, and as a list of words with frequency @xcite . the lexicon extracts all the translation equivalents of a given word across the whole database, providing a list of all languages in which that word has been translated. ## applications of translation alignment in many modern languages, ta is successfully employed in a wide range of nlp tasks. for example, it is essential in word-and phrase-based statistical machine translation (smt) pipelines @xcite ; it can be used to analyze the output of neural machine translation models (nmt) and assess their performance quality @xcite ; to filter and clean noisy parallel corpora @xcite ; to transfer linguistic annotation from one text to its translation, such as semantic role labels, pos tags, named entity tags @xcite @xcite . parallel corpora aligned at word-level can support the work of professional translators @xcite , bilingual lexicon induction @xcite , and word sense disambiguation @xcite . moreover, they provide extremely useful information for vocabulary assimilation and language teaching (vyatkina and boulton, 2017), and for the study of the history of transmission of a corpus @xcite . in the following sections, we will survey the current state of ta research for ancient languages, illustrating how the parallel corpora created on ugarit are used for qualitative and quantitative research. ## conclusions and future work parallel corpora are today's rosetta stones @xcite . they can be used for a variety of philological and computational tasks, as they provide a medium between languages and cultures. this study shows the importance of parallel text processing, specifically in the context of translation alignment, for various activities in the study of low-resource languages. the value of ta emerges in its various applications, which include language learning, nlp development, dictionary extraction, and research on translations and cross-linguistic interactions. most importantly, the development of accurate ta models can significantly contribute to improve the performance of important nlp tasks in ancient languages through the mdeium of annotation projection. for this reason, we plan a significant expansion of monolingual and bilingual corpora for supervised and unsupervised training, in order to improve performance on other ancient languages. moreover, we will test analogous workflows based on annotation projection for other nlp tasks, such as pos tagging and lemmatization. in this sense, the development of accurate sentence alignment workflows is fundamental, as it can significantly enhance the performance of word-alignment models. despite the great success of transformers and language models, we want to emphasize that manually annotated corpora and guidelines are still essential to ensure accurate performance and to detect patterns of error. gold standards and output evaluation require strong disciplinary expertise, especially in scenarios where the research questions are complex. for this reason, as already emphasized by @xcite best efforts in the domain of automatic text processing are achieved score ancient greek latin arabic correct alignment / correct ner 86.63% 82.34% 75.54% incorrect alignment / correct ner 7.26% 12.87% 21.16% correct alignment / incorrect ner 5.28% 3.96% 2.98% incorrect alignment / incorrect ner 0.83% 0.83% 0.33% by multidisciplinary teams, where the contribution of scholars of the language and philologists can provide better information about the idiosyncrasies of the material, and crucially contribute to the evaluation of the results. high-quality philological work is essential for progress in this field, and the only way we can produce reliable tools that will be used by digital humanists and humanists as well.