Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence (2004.03974v2)

Abstract: Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural topic models have shown improvements in overall coherence. Concurrently, contextual embeddings have advanced the state of the art of neural models in general. In this paper, we combine contextualized representations with neural topic models. We find that our approach produces more meaningful and coherent topics than traditional bag-of-words topic models and recent neural models. Our results indicate that future improvements in LLMs will translate into better topic models.

Contextualized Document Embeddings Enhance Topic Coherence

The paper "Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence" introduces a novel approach to enhancing the coherence of topics generated by neural topic models. The authors propose the integration of contextual embeddings derived from pre-trained LLMs into neural topic models, demonstrating significant improvements in topic coherence.

Overview

Traditional topic models, such as those utilizing Bag-of-Words (BoW) representations, fail to capture semantic and syntactic relationships between words, often resulting in incoherent topics. Neural topic models have made strides in improving coherence, yet they still face limitations due to the inability to effectively incorporate context. This paper addresses these challenges by employing contextual embeddings from pre-trained LLMs, specifically those derived from architectures like BERT.

Methodology

The authors extend the Neural ProdLDA, a state-of-the-art topic model, by integrating contextualized representations. The proposed Combined Topic Model (CombinedTM) uses SBERT embeddings, which provide sentence-level contextualization. Importantly, this approach is modular and allows for different contextual embedding models to be utilized without being tied to a specific architecture.

Experimental Evaluation

The paper evaluates the model's performance using five datasets, including 20Newsgroups and Google News, measuring topic coherence and diversity across varying numbers of topics. Metrics such as normalized pointwise mutual information (NPMI) and external word embedding coherence were employed to assess coherence, while inverse Rank-Biased Overlap (RBO) was used for diversity. The CombinedTM consistently outperformed ProdLDA and several other baseline models, including LDA and NVDM, in coherence metrics while maintaining competitive diversity.

Results and Implications

The integration of pre-trained contextual embeddings led to more coherent topic distributions, as evidenced by quantitative metrics and the comparative analysis with baseline models. Contextual information, as captured by models like BERT, proved to be a significant factor in improving the performance of topic models. This paper's findings suggest that as LLMs continue to evolve, their integration into topic modeling could further enhance interpretability and coherence.

Future Directions

Future research could explore the impact of different pre-trained LLMs on topic coherence further. The current paper points to the potential of models like RoBERTa in improving outcomes, hinting at a promising avenue for topic modeling. Moreover, addressing the limitations related to processing longer text sequences remains crucial, given the sentence length constraints inherent in many pre-trained models.

Overall, this paper provides an insightful demonstration of how leveraging contextual embeddings can substantially improve topic modeling's effectiveness, presenting opportunities for advancements in Natural Language Processing applications.