@inproceedings{paccosi-koolen-2026-smelling,
title = "Smelling the Past: Investigating Historical Models for Olfactory Event Extraction",
author = "Paccosi, Teresa and
Koolen, Marijn",
editor = "Sprugnoli, Rachele and
Passarotti, Marco",
booktitle = "Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages ({LT}4{HALA} 2026) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.lt4hala-1.40/",
doi = "10.63317/4otmsnawg3kr",
pages = "389--399",
abstract = "In this paper, we present a series of experiments using historical language models to investigate the impact of pretraining on data that more closely resembles the task domain, focusing on the case study of automatic olfactory event extraction. We tested historical and contemporary pretrained models on the task of extracting olfactory events using a benchmark spanning several centuries. The aim of our research is not only to assess whether historical models can improve performance on this diachronically oriented task, but also to gain deeper insight into the factors influencing model performance through a detailed analysis of performance patterns. We examine potential sources of variation and previously proposed hypotheses to account for lower performance observed in this task, thereby offering a more comprehensive understanding of model behavior in this context."
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%0 Conference Proceedings
%T Smelling the Past: Investigating Historical Models for Olfactory Event Extraction
%A Paccosi, Teresa
%A Koolen, Marijn
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F paccosi-koolen-2026-smelling
%X In this paper, we present a series of experiments using historical language models to investigate the impact of pretraining on data that more closely resembles the task domain, focusing on the case study of automatic olfactory event extraction. We tested historical and contemporary pretrained models on the task of extracting olfactory events using a benchmark spanning several centuries. The aim of our research is not only to assess whether historical models can improve performance on this diachronically oriented task, but also to gain deeper insight into the factors influencing model performance through a detailed analysis of performance patterns. We examine potential sources of variation and previously proposed hypotheses to account for lower performance observed in this task, thereby offering a more comprehensive understanding of model behavior in this context.
%R 10.63317/4otmsnawg3kr
%U https://aclanthology.org/2026.lt4hala-1.40/
%U https://doi.org/10.63317/4otmsnawg3kr
%P 389-399
Markdown (Informal)
[Smelling the Past: Investigating Historical Models for Olfactory Event Extraction](https://aclanthology.org/2026.lt4hala-1.40/) (Paccosi & Koolen, LT4HALA 2026)
ACL