Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling
This work addresses the challenge of adapting unsupervised topic modeling with pre-trained models, offering incremental improvements for researchers in NLP and multilingual applications.
The paper tackled the problem of improving neural topic modeling by fine-tuning pre-trained encoders, finding that fine-tuning on topic classification and integrating it into training enhances topic quality and facilitates cross-lingual transfer.
Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitate zero-shot polylingual topic modeling. However, while it has been widely observed that pre-trained embeddings should be fine-tuned to a given task, it is not immediately clear what supervision should look like for an unsupervised task such as topic modeling. Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling. We consider fine-tuning on auxiliary tasks, constructing a new topic classification task, integrating the topic classification objective directly into topic model training, and continued pre-training. We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality, and that fine-tuning encoder representations on any task is the most important factor for facilitating cross-lingual transfer.