TopicOcean: An Ever-Increasing Topic Model with Meta-Learning
Yuanfeng Song, Yongxin Tong, Siqi Bao, Di Jiang, Hua Ren Wu, Raymond Chi-Wing Wong · 2020
Topic modeling has been intensively studied and widely applied in both academia and industry in the last decade. In the literature, topic models usually need to be trained from scratch for each individual corpus. Hence, the wisdom of the crowd (i.e., topic models previously trained based upon other corpora) is abandoned. Since a massive amount of in-domain data, considerable computational cost, and human labour are involved in obtaining a high-quality topic model, training from scratch for each new corpus is a huge waste of resources. In this paper, we propose the novel TopicOcean framework, which aims to integrate well-trained topic models and transfer the knowledge of accumulated topics to new corpora in order to improve the quality of their topic models. We first propose a method of constructing the ever-increasing TopicOcean, and then propose a meta-learning mechanism that transfers the meta-level knowledge (i.e., topics) in TopicOcean to the scenario of topic modeling on new corpora. Comprehensive experiments validate that the TopicOcean framework can significantly outperform the state-of-the-art (53.77% perplexity improvement on a temporal-shift corpus and 29.24% improvement on a domain-shift corpus). The well-trained high-quality topic models used to construct TopicOcean have been opensourced to promote further research.11The well-trained topic models can be accessed at Github (https://github.com/baidu/Familia/blob/master/model/download_model.sh).