One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
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Abstract (arXiv)
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.
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References · 11
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2019Topic Modeling in Embedding Spacescited by 224
- 2020Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherencecited by 121
- 2018A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizationscited by 99
- 2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonflycited by 67
- 2020Cross-lingual Contextualized Topic Models with Zero-shot Learningcited by 67
- 2019Topic Modeling with Wasserstein Autoencoderscited by 53
- 2021Topic Modelling Meets Deep Neural Networks: A Surveycited by 51
- 2018Coherence-Aware Neural Topic Modelingcited by 26
- 2016Bibliographic Analysis with the Citation Network Topic Modelcited by 5
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