Improving Neural Topic Modeling with Semantically-Grounded Soft Label Distributions
arXiv:2602.17907v1 Announce Type: cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity. In this work, we propose a novel approach to construct semantically-grounded soft label targets using Language Models (LMs) by projecting the next token probabilities, conditioned on a specialized prompt, onto a pre-defined vocabulary to obtain contextually enriched supervision signals. By training the topic models to reconstruct the soft labels using the LM hidden states, our method produces higher-quality topics that are more closely aligned with the underlying thematic structure of the corpus. Experiments on three datasets show that our method achieves substantial improvements in topic coherence, purity over existing baselines. Additionally, we also introduce a retrieval-based metric, which shows that our approach significantly outperforms e
arXiv:2602.17907v1 Announce Type: cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity. In this work, we propose a novel approach to construct semantically-grounded soft label targets using Language Models (LMs) by projecting the next token probabilities, conditioned on a specialized prompt, onto a pre-defined vocabulary to obtain contextually enriched supervision signals. By training the topic models to reconstruct the soft labels using the LM hidden states, our method produces higher-quality topics that are more closely aligned with the underlying thematic structure of the corpus. Experiments on three datasets show that our method achieves substantial improvements in topic coherence, purity over existing baselines. Additionally, we also introduce a retrieval-based metric, which shows that our approach significantly outperforms existing methods in identifying semantically similar documents, highlighting its effectiveness for retrieval-oriented applications.
Executive Summary
This article proposes a novel approach to improve neural topic modeling by using semantically-grounded soft label distributions. The method leverages language models to generate contextually enriched supervision signals, resulting in higher-quality topics that better align with the underlying thematic structure of the corpus. Experiments on three datasets demonstrate substantial improvements in topic coherence, purity, and retrieval-oriented applications.
Key Points
- ▸ Introduction of semantically-grounded soft label distributions for neural topic modeling
- ▸ Utilization of language models to generate contextually enriched supervision signals
- ▸ Substantial improvements in topic coherence, purity, and retrieval-oriented applications
Merits
Improved Topic Quality
The proposed method produces higher-quality topics that are more closely aligned with the underlying thematic structure of the corpus.
Enhanced Retrieval Performance
The approach significantly outperforms existing methods in identifying semantically similar documents, highlighting its effectiveness for retrieval-oriented applications.
Demerits
Computational Complexity
The use of language models to generate soft label distributions may increase computational complexity and require significant computational resources.
Expert Commentary
The proposed method represents a significant advancement in neural topic modeling, as it addresses the limitations of traditional approaches by incorporating contextual information and semantic relationships. The use of language models to generate soft label distributions is a innovative approach that can be applied to various natural language processing tasks. However, the computational complexity of the method may be a concern, and future research should focus on optimizing the approach for large-scale datasets.
Recommendations
- ✓ Future research should investigate the application of the proposed method to other natural language processing tasks, such as text classification and sentiment analysis.
- ✓ The development of more efficient algorithms and optimization techniques is necessary to reduce the computational complexity of the approach and enable its deployment in real-world applications.
