Neural Variational Inference for Text Processing (1511.06038v4)

Abstract: Recent advances in neural variational inference have spawned a renaissance in deep latent variable models. In this paper we introduce a generic variational inference framework for generative and conditional models of text. While traditional variational methods derive an analytic approximation for the intractable distributions over latent variables, here we construct an inference network conditioned on the discrete text input to provide the variational distribution. We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering. Our neural variational document model combines a continuous stochastic document representation with a bag-of-words generative model and achieves the lowest reported perplexities on two standard test corpora. The neural answer selection model employs a stochastic representation layer within an attention mechanism to extract the semantics between a question and answer pair. On two question answering benchmarks this model exceeds all previous published benchmarks.

Neural Variational Inference for Text Processing

The paper "Neural Variational Inference for Text Processing" introduces a generic framework for applying variational inference to generative and conditional models of text. This research leverages recent advances in neural variational inference to improve upon traditional methods, which often struggle with intractable distributions over latent variables. The authors propose a neural network-based inference network conditioned on discrete text input to construct the variational distribution.

Methodology and Framework

The focus of the paper is the development of a framework inspired by the variational auto-encoder (VAE), which constructs an inference network using deep neural networks to approximate intractable distributions over latent variables. This approach allows the framework to learn complex non-linear distributions and handle structured inputs, such as word sequences.

1. Neural Variational Document Model (NVDM): This model integrates a continuous stochastic document representation with a bag-of-words generative model. A multilayer perceptron (MLP) encoder compresses the document representation into a continuous latent distribution, while a softmax decoder reconstructs the document. NVDM achieves state-of-the-art perplexities on standard test corpora, such as 20NewsGroups and RCV1-v2 datasets, illustrating its efficacy in document modeling.
2. Neural Answer Selection Model (NASM): Designed for supervised question answering, NASM incorporates a latent stochastic layer into an attention mechanism to model the semantics of question-answer pairs. The model outperforms previous approaches on question answering tasks by learning pair-specific representations, thus improving predictive performance.

Numerical Results

• NVDM Results: The NVDM reported the lowest perplexity scores on the 20NewsGroups and RCV1-v2 datasets compared to several baselines, such as LDA, RSM, docNADE, and models based on Sigmoid Belief Networks. For instance, the perplexity for NVDM on the RCV1-v2 dataset with 50 latent dimensions is 563, outperforming other models with similar complexity.
• NASM Results: On the QASent and WikiQA datasets, NASM set new benchmarks for mean average precision (MAP) and mean reciprocal rank (MRR), outperforming LSTM and attention-based models when combined with a lexical overlap feature, further affirming its superiority.

Implications and Future Directions

This research underscores the promise of neural variational inference for diverse text processing tasks, offering improvements in both unsupervised and supervised learning settings. In practice, such models can be deployed to enhance document categorization, topic modeling, and information retrieval systems, where robust semantic understanding is crucial.

On a theoretical front, the framework paves the way for integrating latent variable models with various neural network architectures, including CNNs and RNNs, offering a flexible foundation for further research in deep generative modeling.

Looking forward, the implications for AI development are significant. The ability to model complex semantic relationships in text can enhance machine comprehension and the development of sophisticated AI applications, such as conversational agents and semantic search engines, where understanding nuanced language is key.

Overall, this paper provides a robust foundation for expanding the capabilities of neural variational inference in natural language processing, setting the stage for continued advancements in AI-driven text analysis.