Lexical semantic change detection is a new and innovative research field. The optimal fine-tuning of models including pre-and postprocessing is largely unclear. We optimize existing models by (i) pre-training on large corpora and refining on diachronic target corpora tackling the notorious small data problem, and (ii) applying post-processing transformations that have been shown to improve performance on synchronic tasks. Our results provide a guide for the application and optimization of lexical semantic change detection models across various learning scenarios. * Authors contributed equally, and their ordering was determined randomly.

Lexical semantic change detection is a new and innovative research field. The optimal fine-tuning of models including pre-and postprocessing is largely unclear. We optimize existing models by (i) pre-training on large corpora and refining on diachronic target corpora tackling the notorious small data problem, and (ii) applying post-processing transformations that have been shown to improve performance on synchronic tasks. Our results provide a guide for the application and optimization of lexical semantic change detection models across various learning scenarios. * Authors contributed equally, and their ordering was determined randomly.