One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization

Autor:	Terragni, Silvia, Harrando, Ismail, Lisena, Pasquale, Troncy, Raphael, Fersini, Elisabetta
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Topic models are statistical methods that extract underlying topics from document collections. When performing topic modeling, a user usually desires topics that are coherent, diverse between each other, and that constitute good document representations for downstream tasks (e.g. document classification). In this paper, we conduct a multi-objective hyperparameter optimization of three well-known topic models. The obtained results reveal the conflicting nature of different objectives and that the training corpus characteristics are crucial for the hyperparameter selection, suggesting that it is possible to transfer the optimal hyperparameter configurations between datasets. Comment: 17 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.07631 Zobrazit plný text záznamu View this record from Arxiv