PILA: A Historical-Linguistic Dataset of Proto-Italic and Latin

Stephen Bothwell, Brian DuSell, David Chiang, Brian Krostenko


Abstract
Computational historical linguistics seeks to systematically understand processes of sound change, including during periods at which little to no formal recording of language is attested. At the same time, few computational resources exist which deeply explore phonological and morphological connections between proto-languages and their descendants. This is particularly true for the family of Italic languages. To assist historical linguists in the study of Italic sound change, we introduce the Proto-Italic to Latin (PILA) dataset, which consists of roughly 3,000 pairs of forms from Proto-Italic and Latin. We provide a detailed description of how our dataset was created and organized. Then, we exhibit PILA’s value in two ways. First, we present baseline results for PILA on a pair of traditional computational historical linguistics tasks. Second, we demonstrate PILA’s capability for enhancing other historical-linguistic datasets through a dataset compatibility study.
Anthology ID:
2024.lrec-main.1116
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
12749–12760
Language:
URL:
https://aclanthology.org/2024.lrec-main.1116
DOI:
Bibkey:
Cite (ACL):
Stephen Bothwell, Brian DuSell, David Chiang, and Brian Krostenko. 2024. PILA: A Historical-Linguistic Dataset of Proto-Italic and Latin. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12749–12760, Torino, Italia. ELRA and ICCL.
Cite (Informal):
PILA: A Historical-Linguistic Dataset of Proto-Italic and Latin (Bothwell et al., LREC-COLING 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.lrec-main.1116.pdf