Autoencoding Variational Inference For Topic Models (1703.01488v1)

Abstract: Topic models are one of the most popular methods for learning representations of text, but a major challenge is that any change to the topic model requires mathematically deriving a new inference algorithm. A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven diffi- cult to apply to topic models in practice. We present what is to our knowledge the first effective AEVB based inference method for latent Dirichlet allocation (LDA), which we call Autoencoded Variational Inference For Topic Model (AVITM). This model tackles the problems caused for AEVB by the Dirichlet prior and by component collapsing. We find that AVITM matches traditional methods in accuracy with much better inference time. Indeed, because of the inference network, we find that it is unnecessary to pay the computational cost of running variational optimization on test data. Because AVITM is black box, it is readily applied to new topic models. As a dramatic illustration of this, we present a new topic model called ProdLDA, that replaces the mixture model in LDA with a product of experts. By changing only one line of code from LDA, we find that ProdLDA yields much more interpretable topics, even if LDA is trained via collapsed Gibbs sampling.

• The paper introduces AVITM, which applies autoencoding variational Bayes to latent Dirichlet allocation, achieving significantly faster inference.
• It overcomes challenges like component collapsing and offers a flexible, black-box approach adaptable to various topic models.
• Empirical results on datasets such as 20 Newsgroups and RCV1 validate enhanced topic coherence and competitive accuracy over traditional methods.

Autoencoding Variational Inference for Topic Models

The paper "Autoencoding Variational Inference for Topic Models" by Akash Srivastava and Charles Sutton investigates a novel application of autoencoding variational Bayes (AEVB) to latent Dirichlet allocation (LDA), presenting an enhanced method termed Autoencoded Variational Inference for Topic Models (AVITM). This work tackles longstanding challenges in the application of AEVB to topic models, notably those posed by the Dirichlet prior and component collapsing. The approach's efficacy is demonstrated through significant reductions in inference time while maintaining accuracy levels competitive with traditional methods.

The research highlights several key achievements:

1. Inference Efficacy and Efficiency: AVITM exhibits superior inference speed relative to traditional mean-field methods without sacrificing accuracy. This is primarily due to the inference network, which efficiently approximates posterior distributions for new data, negating the need for additional variational optimization.
2. Flexibility and Ease of Application: Being black-box in nature, AVITM presents a significant advantage as it can be easily adapted to various topic models. This is particularly illustrated with the introduction of ProdLDA, a product of experts model that enhances topic interpretability with minimal code modifications from traditional LDA implementations.
3. Quantitative and Qualitative Validation: Empirical results on datasets like 20 Newsgroups and RCV1 validate the improved topic coherence offered by ProdLDA, showcasing enhancements over standard LDA trained with collapsed Gibbs sampling.

The discussion on the challenges in applying AEVB such as component collapsing is noteworthy. The paper proposes strategies to mitigate these, including higher learning rates and batch normalization, specifically tailored for topic models. Additionally, the use of a Laplace approximation to handle the Dirichlet prior marks a significant step in bridging theoretical limitations with practical applications.

Implications and Future Directions

Practically, this work could lead to the development of faster and more flexible topic modeling tools applicable in diverse domains such as literature discovery, content recommendation, and more. Theoretically, it invites further exploration into integrating advanced neural approaches with traditional Bayesian models.

Future developments might explore the extension of AVITM to more complex models like dynamic or correlated topic models, thereby broadening the applicability of AEVB while maintaining computational efficiency.

Overall, the research contributes a robust methodology to the field of topic modeling, providing a scalable and efficient tool that aligns with modern computational demands. It opens potential pathways for integrating neural network architectures with graphical model inference, paving the way for novel applications in text analysis and beyond.