Nostra Domina at EvaLatin 2024: Improving Latin Polarity Detection through Data Augmentation

Stephen Bothwell, Abigail Swenor, David Chiang

Abstract
This paper describes submissions from the team Nostra Domina to the EvaLatin 2024 shared task of emotion polarity detection. Given the low-resource environment of Latin and the complexity of sentiment in rhetorical genres like poetry, we augmented the available data through automatic polarity annotation. We present two methods for doing so on the basis of the k-means algorithm, and we employ a variety of Latin large language models (LLMs) in a neural architecture to better capture the underlying contextual sentiment representations. Our best approach achieved the second highest macro-averaged Macro-F1 score on the shared task’s test set.
Anthology ID:
2024.lt4hala-1.25
Volume:
Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Rachele Sprugnoli, Marco Passarotti
Venues:
LT4HALA | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
215–222
Language:
URL:
https://aclanthology.org/2024.lt4hala-1.25
DOI:
Bibkey:
Cite (ACL):
Stephen Bothwell, Abigail Swenor, and David Chiang. 2024. Nostra Domina at EvaLatin 2024: Improving Latin Polarity Detection through Data Augmentation. In Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024, pages 215–222, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Nostra Domina at EvaLatin 2024: Improving Latin Polarity Detection through Data Augmentation (Bothwell et al., LT4HALA-WS 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.lt4hala-1.25.pdf
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