Discovering Discrete Latent Topics with Neural Variational Inference (1706.00359v2)

Abstract: Topic models have been widely explored as probabilistic generative models of documents. Traditional inference methods have sought closed-form derivations for updating the models, however as the expressiveness of these models grows, so does the difficulty of performing fast and accurate inference over their parameters. This paper presents alternative neural approaches to topic modelling by providing parameterisable distributions over topics which permit training by backpropagation in the framework of neural variational inference. In addition, with the help of a stick-breaking construction, we propose a recurrent network that is able to discover a notionally unbounded number of topics, analogous to Bayesian non-parametric topic models. Experimental results on the MXM Song Lyrics, 20NewsGroups and Reuters News datasets demonstrate the effectiveness and efficiency of these neural topic models.

• The paper introduces a neural variational inference framework for topic models that optimizes parameterizable distributions through neural networks.
• It presents innovative architectures—Gaussian Softmax, Gaussian Stick-Breaking, and Recurrent Stick-Breaking—that enable adaptive and sparse topic discovery.
• Empirical results on multiple datasets demonstrate that the proposed models achieve lower perplexity compared to traditional baselines like LDA.

Overview of "Discovering Discrete Latent Topics with Neural Variational Inference"

The paper presents a novel approach to topic modeling by leveraging neural variational inference frameworks, proposing a suite of neural models that explore discrete latent topics. This research leverages recent advancements in neural network architectures, specifically within the field of variational autoencoders (VAEs), to address the limitations of traditional probabilistic topic modeling techniques such as Latent Dirichlet Allocation (LDA).

Key Contributions

1. Neural Variational Inference for Topic Models: The authors innovate by utilizing parameterizable distributions through neural networks to facilitate topic modeling. They employ neural variational inference to optimize over these distributions, contrasting with conventional models that rely on closed-form solutions and typically complex inference procedures.
2. Stick-Breaking Construction: The paper introduces a novel application of stick-breaking processes within neural architectures. A recurrent stick-breaking process is used here as an analog to Bayesian non-parametric methods, facilitating an adaptive, notionally unbounded number of topics, a significant stride beyond the fixed topic counts in many classical models.
3. Three Architectural Constructions:
   • Gaussian Softmax (GSM): Implements a finite topic distribution using a Gaussian random vector processed through a softmax function, offering a direct but limited approach to capturing topic probabilities.
   • Gaussian Stick-Breaking (GSB): Employs a mechanism that introduces sparsity into topic distributions through a stick-breaking process, biasing the system toward sparser structures.
   • Recurrent Stick-Breaking (RSB): Utilizes an RNN to systematically generate topic proportions, effectively modeling a non-parametric distribution over the topics and enabling dynamic topic count adjustments.

Experimental Evaluation

The authors deliver empirical validation across multiple datasets, including MXM Song Lyrics, 20NewsGroups, and Reuters, highlighting the efficiency and effectiveness of their models. Notably, they demonstrate that their neural topic models outperform several established baseline models, including the OnlineLDA and NVLDA, in terms of perplexity—a common measure of model quality in topic modeling.

Implications and Future Directions

The findings suggest that incorporating neural networks into probabilistic models, specifically within the field of topic modeling, yields models that are both scalable and interpretable while maintaining flexibility in topic discovery. The proposed recurrent stick-breaking neural mechanism opens paths for exploration in other unsupervised learning tasks that require non-parametric interpretations or dynamic modeling capabilities.

Looking forward, this paper's methods could inspire further developments in adaptive, scalable unsupervised learning techniques leveraging neural architectures. The compatibility of deep learning techniques with traditional probabilistic models points towards an exciting integration of paradigms that may solve increasingly complex tasks in natural language processing and beyond. Such models hold promise not just due to their immediate computational benefits but also because they underline a growing concordance between statistical rigor and neural approximation—ushering in models that learn more efficiently from data.

Overall, this paper offers substantial advancements in neural topic modeling, with implications that extend to broader AI research and application domains. Future work may focus on refining these architectures, exploring their application in different domains, and investigating their integration with other machine learning paradigms for improved performance and utility.