@inproceedings{keersmaekers-mercelis-2024-adapting,
title = "Adapting transformer models to morphological tagging of two highly inflectional languages: a case study on {A}ncient {G}reek and {L}atin",
author = "Keersmaekers, Alek and
Mercelis, Wouter",
editor = "Pavlopoulos, John and
Sommerschield, Thea and
Assael, Yannis and
Gordin, Shai and
Cho, Kyunghyun and
Passarotti, Marco and
Sprugnoli, Rachele and
Liu, Yudong and
Li, Bin and
Anderson, Adam",
booktitle = "Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)",
month = aug,
year = "2024",
address = "Hybrid in Bangkok, Thailand and online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.ml4al-1.17",
pages = "165--176",
abstract = "Natural language processing for Greek and Latin, inflectional languages with small corpora, requires special techniques. For morphological tagging, transformer models show promising potential, but the best approach to use these models is unclear. For both languages, this paper examines the impact of using morphological lexica, training different model types (a single model with a combined feature tag, multiple models for separate features, and a multi-task model for all features), and adding linguistic constraints. We find that, although simply fine-tuning transformers to predict a monolithic tag may already yield decent results, each of these adaptations can further improve tagging accuracy.",
}
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%0 Conference Proceedings
%T Adapting transformer models to morphological tagging of two highly inflectional languages: a case study on Ancient Greek and Latin
%A Keersmaekers, Alek
%A Mercelis, Wouter
%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 keersmaekers-mercelis-2024-adapting
%X Natural language processing for Greek and Latin, inflectional languages with small corpora, requires special techniques. For morphological tagging, transformer models show promising potential, but the best approach to use these models is unclear. For both languages, this paper examines the impact of using morphological lexica, training different model types (a single model with a combined feature tag, multiple models for separate features, and a multi-task model for all features), and adding linguistic constraints. We find that, although simply fine-tuning transformers to predict a monolithic tag may already yield decent results, each of these adaptations can further improve tagging accuracy.
%U https://aclanthology.org/2024.ml4al-1.17
%P 165-176
Markdown (Informal)
[Adapting transformer models to morphological tagging of two highly inflectional languages: a case study on Ancient Greek and Latin](https://aclanthology.org/2024.ml4al-1.17) (Keersmaekers & Mercelis, ML4AL-WS 2024)
ACL