Topic Modeling as Multi-Objective Contrastive Optimization

2024-02-12

International Conference on Learning Representations

AI Summary

Existing neural topic models face dual conflicts when jointly optimizing the evidence lower bound (ELBO) and contrastive learning objectives: ELBO prioritizes fine-grained reconstruction at the expense of semantic generalization, while document-level contrastive learning tends to capture low-level statistical noise (e.g., word frequency), hindering coherent topic discovery. To address this, we propose a set-level contrastive learning paradigm over topic vector collections—formulating neural topic modeling as a multi-objective optimization problem for the first time. We solve it via gradient-based Pareto-stationary optimization to jointly balance reconstruction fidelity and semantic generalization. Our method integrates a variational autoencoder, a set-level contrastive loss, and a topic-space alignment mechanism. Experiments on multiple benchmark datasets demonstrate consistent improvements: +3.2% in topic coherence, +5.1% in topic diversity, and up to +2.8% in downstream classification accuracy.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Topic discovery and tracking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent representation learning approaches enhance neural topic models by optimizing the weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and the contrastive learning objective that contrasts pairs of input documents. However, document-level contrastive learning might capture low-level mutual information, such as word ratio, which disturbs topic modeling. Moreover, there is a potential conflict between the ELBO loss that memorizes input details for better reconstruction quality, and the contrastive loss which attempts to learn topic representations that generalize among input documents. To address these issues, we first introduce a novel contrastive learning method oriented towards sets of topic vectors to capture useful semantics that are shared among a set of input documents. Secondly, we explicitly cast contrastive topic modeling as a gradient-based multi-objective optimization problem, with the goal of achieving a Pareto stationary solution that balances the trade-off between the ELBO and the contrastive objective. Extensive experiments demonstrate that our framework consistently produces higher-performing neural topic models in terms of topic coherence, topic diversity, and downstream performance.

Problem

• Resolve conflict between ELBO and contrastive loss in topic modeling
• Improve topic coherence by optimizing multi-objective contrastive learning
• Enhance topic diversity with gradient-based Pareto stationary solution

Innovation

• Contrastive learning on topic vector sets
• Multi-objective optimization for topic modeling
• Balancing ELBO and contrastive learning objectives