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    <abstract>The Machine-Actionable Ancient Text (MAAT) Corpus is a new resource providing training and evaluation data for restoring lacunae in ancient Greek, Latin, and Coptic texts. Current text restoration systems require large amounts of data for training and task-relevant means for evaluation. The MAAT Corpus addresses this need by converting texts available in EpiDoc XML format into a machine-actionable format that preserves the most textually salient aspects needed for machine learning: the text itself, lacunae, and textual restorations. Structured test cases are generated from the corpus that align with the actual text restoration task performed by papyrologists and epigraphist, enabling more realistic evaluation than the synthetic tasks used previously. The initial 1.0 beta release contains approximately 134,000 text editions, 178,000 text blocks, and 750,000 individual restorations, with Greek and Latin predominating. This corpus aims to facilitate the development of computational methods to assist scholars in accurately restoring ancient texts.</abstract>
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%0 Conference Proceedings
%T A new machine-actionable corpus for ancient text restoration
%A Fitzgerald, Will
%A Barney, Justin
%Y Pavlopoulos, John
%Y Sommerschield, Thea
%Y Assael, Yannis
%Y Gordin, Shai
%Y Cho, Kyunghyun
%Y Passarotti, Marco
%Y Sprugnoli, Rachele
%Y Liu, Yudong
%Y Li, Bin
%Y Anderson, Adam
%S Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Hybrid in Bangkok, Thailand and online
%F fitzgerald-barney-2024-new
%X The Machine-Actionable Ancient Text (MAAT) Corpus is a new resource providing training and evaluation data for restoring lacunae in ancient Greek, Latin, and Coptic texts. Current text restoration systems require large amounts of data for training and task-relevant means for evaluation. The MAAT Corpus addresses this need by converting texts available in EpiDoc XML format into a machine-actionable format that preserves the most textually salient aspects needed for machine learning: the text itself, lacunae, and textual restorations. Structured test cases are generated from the corpus that align with the actual text restoration task performed by papyrologists and epigraphist, enabling more realistic evaluation than the synthetic tasks used previously. The initial 1.0 beta release contains approximately 134,000 text editions, 178,000 text blocks, and 750,000 individual restorations, with Greek and Latin predominating. This corpus aims to facilitate the development of computational methods to assist scholars in accurately restoring ancient texts.
%R 10.18653/v1/2024.ml4al-1.7
%U https://aclanthology.org/2024.ml4al-1.7
%U https://doi.org/10.18653/v1/2024.ml4al-1.7
%P 56-60

Markdown (Informal)

[A new machine-actionable corpus for ancient text restoration](https://aclanthology.org/2024.ml4al-1.7) (Fitzgerald & Barney, ML4AL-WS 2024)

• A new machine-actionable corpus for ancient text restoration (Fitzgerald & Barney, ML4AL-WS 2024)

ACL

• Will Fitzgerald and Justin Barney. 2024. A new machine-actionable corpus for ancient text restoration. In Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024), pages 56–60, Hybrid in Bangkok, Thailand and online. Association for Computational Linguistics.