Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations (2202.04582v1)

Abstract: Topic models have been the prominent tools for automatic topic discovery from text corpora. Despite their effectiveness, topic models suffer from several limitations including the inability of modeling word ordering information in documents, the difficulty of incorporating external linguistic knowledge, and the lack of both accurate and efficient inference methods for approximating the intractable posterior. Recently, pretrained LLMs (PLMs) have brought astonishing performance improvements to a wide variety of tasks due to their superior representations of text. Interestingly, there have not been standard approaches to deploy PLMs for topic discovery as better alternatives to topic models. In this paper, we begin by analyzing the challenges of using PLM representations for topic discovery, and then propose a joint latent space learning and clustering framework built upon PLM embeddings. In the latent space, topic-word and document-topic distributions are jointly modeled so that the discovered topics can be interpreted by coherent and distinctive terms and meanwhile serve as meaningful summaries of the documents. Our model effectively leverages the strong representation power and superb linguistic features brought by PLMs for topic discovery, and is conceptually simpler than topic models. On two benchmark datasets in different domains, our model generates significantly more coherent and diverse topics than strong topic models, and offers better topic-wise document representations, based on both automatic and human evaluations.

Overview of "Topic Discovery via Latent Space Clustering of Pretrained LLM Representations"

The research paper entitled "Topic Discovery via Latent Space Clustering of Pretrained LLM Representations" presents a novel approach to topic discovery by leveraging the inherent strengths of pretrained LLMs (PLMs) like BERT. Historically, topic models such as Latent Dirichlet Allocation (LDA) have been extensively used for extracting topics from text corpora. However, these models often suffer from limitations such as the "bag-of-words" assumption, lack of integration with external linguistic knowledge, and the use of complex, sometimes inefficient, inference methods. This paper proposes an alternative by utilizing the superior text representations of PLMs.

Challenges in Using PLMs for Topic Discovery

The authors identify several challenges in deploying PLM embeddings for topic discovery. Firstly, the native PLM embedding space does not naturally exhibit the discrete clustering structure required for robust topic modeling. PLMs are typically trained to model language with a high-dimensional embedding space, which inherently lacks distinct clusters suitable for traditional clustering techniques. Secondly, the high dimensionality of embeddings poses computational difficulties, often referred to as the "curse of dimensionality," that can negatively affect clustering performance. Finally, the paper notes a challenge in obtaining high-quality document embeddings directly from PLMs without fine-tuning, which is crucial for effective topic discovery.

Proposed Solution

To address the aforementioned challenges, the authors propose a joint latent space learning and clustering framework known as TopClus. This method operates by first learning a lower-dimensional, spherical latent space where word and document embeddings can be jointly modeled. Within this latent space, angular similarity measures are preferred over traditional Euclidean metrics, facilitating more coherent topic representations.

TopClus integrates three core objectives to ensure the quality of the discovered topics:

1. Clustering Loss: This loss term promotes the formation of distinct and well-defined clusters in the latent space, thereby facilitating the learning of coherent topics.
2. Topical Reconstruction Loss: This ensures that topics not only are interpretable but also effectively summarize the corresponding documents, maintaining semantic fidelity to the original content.
3. Embedding Space Preservation Loss: This objective ensures that the semantic relationships present in the original high-dimensional space are retained in the latent space.

Empirical Evaluation

The paper empirically validates TopClus on two benchmark datasets, New York Times (NYT) and Yelp reviews, demonstrating its superior performance against baselines including LDA, CorEx, ETM, and BERTopic. The evaluation metrics include UMass and UCI for topic coherence, intrusion tests for human validation of coherence, and a measure of topic diversity. TopClus not only generates the most coherent and diverse topics but also improves document clustering capabilities, as evidenced by higher NMI scores.

Implications and Future Work

The proposed framework exemplifies a significant step forward in topic modeling by harnessing PLM representations without the shortcomings of traditional topic models. This method holds potential for extensions to hierarchical topic structures and possibly other related applications such as taxonomy construction and weakly-supervised text classification.

Future research could explore integrating this approach with varied PLMs and advanced clustering techniques to further enhance the accuracy and applicability of topic discovery tasks across diverse text domains. Furthermore, addressing potential biases in PLMs, which may carry over into topic discovery tasks, represents an ethical consideration and area for future exploration.

In summary, this paper offers a compelling alternative to traditional topic models, leveraging advancements in PLMs to overcome longstanding limitations and creatively applying latent space methodologies to achieve improvements in topic coherence and diversity.