@inproceedings{pham-etal-2024-neuromax,
    title = "{N}euro{M}ax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization",
    author = "Pham, Duy-Tung  and
      Nguyen Vu, Thien Trang  and
      Nguyen, Tung  and
      Ngo, Linh Van  and
      Nguyen, Duc Anh  and
      Nguyen, Thien Huu",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-emnlp.457/",
    doi = "10.18653/v1/2024.findings-emnlp.457",
    pages = "7758--7772",
    abstract = "Recent advances in neural topic models have concentrated on two primary directions: the integration of the inference network (encoder) with a pre-trained language model (PLM) and the modeling of the relationship between words and topics in the generative model (decoder). However, the use of large PLMs significantly increases inference costs, making them less practical for situations requiring low inference times. Furthermore, it is crucial to simultaneously model the relationships between topics and words as well as the interrelationships among topics themselves. In this work, we propose a novel framework called NeuroMax (**Neur**al T**o**pic Model with **Max**imizing Mutual Information with Pretrained Language Model and Group Topic Regularization) to address these challenges. NeuroMax maximizes the mutual information between the topic representation obtained from the encoder in neural topic models and the representation derived from the PLM. Additionally, NeuroMax employs optimal transport to learn the relationships between topics by analyzing how information is transported among them. Experimental results indicate that NeuroMax reduces inference time, generates more coherent topics and topic groups, and produces more representative document embeddings, thereby enhancing performance on downstream tasks."
}