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Smelling the Past: Investigating Historical Models for Olfactory Event Extraction

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.