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.
One configuration to rule them all? Towards hyperparameter transfer in topic models using multi-objective Bayesian optimization
Submitted to ArXiV, 15 February 2022
Type:
Conférence
Date:
2022-02-15
Department:
Data Science
Eurecom Ref:
6820
Copyright:
© EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted to ArXiV, 15 February 2022 and is available at :
See also:
PERMALINK : https://www.eurecom.fr/publication/6820