arXiv Open Access 2023

Logion: Machine Learning for Greek Philology

Charlie Cowen-Breen Creston Brooks Johannes Haubold Barbara Graziosi

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Abstrak

This paper presents machine-learning methods to address various problems in Greek philology. After training a BERT model on the largest premodern Greek dataset used for this purpose to date, we identify and correct previously undetected errors made by scribes in the process of textual transmission, in what is, to our knowledge, the first successful identification of such errors via machine learning. Additionally, we demonstrate the model's capacity to fill gaps caused by material deterioration of premodern manuscripts and compare the model's performance to that of a domain expert. We find that best performance is achieved when the domain expert is provided with model suggestions for inspiration. With such human-computer collaborations in mind, we explore the model's interpretability and find that certain attention heads appear to encode select grammatical features of premodern Greek.

Topik & Kata Kunci

cs.CL cs.LG

Penulis (4)

Charlie Cowen-Breen

Creston Brooks

Johannes Haubold

Barbara Graziosi

Format Sitasi

APA MLA BibTeX

Cowen-Breen, C., Brooks, C., Haubold, J., Graziosi, B. (2023). Logion: Machine Learning for Greek Philology. https://arxiv.org/abs/2305.01099

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓