Generating Sentences from a Continuous Space (1511.06349v4)

Abstract: The standard recurrent neural network LLM (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work, we introduce and study an RNN-based variational autoencoder generative model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences. We present techniques for solving the difficult learning problem presented by this model, demonstrate its effectiveness in imputing missing words, explore many interesting properties of the model's latent sentence space, and present negative results on the use of the model in LLMing.

Generating Sentences from a Continuous Space

This paper, authored by Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, and Samy Bengio, presents an investigation into the generative modeling of sentences using a variational autoencoder (VAE) framework. The paper extends recurrent neural network LLMs (RNNLMs) by integrating a continuous latent variable that captures global sentence-level features, thus addressing the limitations of traditional RNNLMs in representing holistic sentence attributes.

Motivation and Background

While RNNLMs have demonstrated efficacy in generating sequences with intricate dependencies, their word-by-word generative process does not provide an explicit global sentence representation. This restricts their ability to encapsulate high-level properties such as syntax, style, and topic within a unified vector. To circumvent these limitations, the authors propose a VAE-based model that employs distributed latent representations of entire sentences. This framework draws on previous successes in variational inference and generative models from other domains, such as images and speech.

Model Architecture

The proposed model integrates single-layer LSTM networks as both the encoder and decoder within the VAE framework. The VAE's encoder maps an input sentence to a latent space, from which the decoder reconstructs the sentence. This architecture maintains the flexibility of RNNLMs while enhancing their capacity to model global features via the latent variable. The model ensures that the latent space representations retain a regular geometric structure by using a probabilistic approach, which enables the generation of both diverse and coherent sentences via sampling.

A significant contribution of this work is the introduction of techniques to address optimization challenges inherent in training VAE models for natural language. Specifically, they employ KL cost annealing and word dropout to ensure that the model learns to utilize the latent variable effectively. KL cost annealing gradually increases the weight of the KL divergence term in the objective function, while word dropout encourages the model to rely on the latent representation by masking input words during training.

Experimental Results

Experiments are conducted on both standard LLMing tasks using the Penn Treebank dataset and sentence imputation tasks using the Books Corpus. In LLMing, the proposed VAE model achieves comparable performance to the baseline RNNLM, despite the added complexity of incorporating a global latent variable. In the task of imputing missing words, the VAE shows marked improvements over RNNLMs, producing more diverse and plausible sentence completions. This is evidenced by a novel adversarial evaluation strategy, which reveals that imputed sentences generated by the VAE are harder to distinguish from true sentences compared to those generated by RNNLMs.

Analysis of Latent Space

Qualitative analyses of the VAE model highlight its ability to generate coherent sentences through deterministic decoding from continuous latent space samples. Homotopies between sentence pairs demonstrate smooth transitions and grammatical intermediate sentences, showcasing the model's capability to capture meaningful syntactic and semantic features.

Theoretical and Practical Implications

The findings underscore the potential of VAEs to extend beyond traditional RNNLM capabilities by embedding global sentence features within a continuous latent space. This enhanced representation facilitates improved performance in tasks requiring an understanding of holistic sentence properties, such as text completion and sentiment analysis. The success of the proposed model in leveraging variational inference principles opens avenues for further research into more sophisticated generative models for text, including those that disentangle style and content or operate under adversarial training paradigms.

Future Directions

Future research could explore the following directions:

1. Factorization of Latent Variables: Decomposing the latent variable into separate components for content and stylistic features can enable more controlled text generation.
2. Conditional Sentence Generation: Extending the model to generate sentences conditioned on additional external features such as sentiment or politeness.
3. Semi-supervised Learning: Leveraging the VAE framework for tasks like textual entailment, where labeled data is limited.
4. Adversarial Training: Experimenting with fully adversarial training objectives to further enhance the quality and diversity of generated text.

Conclusion

This paper presents a significant step towards integrating continuous latent space representations in the generative modeling of text. The proposed VAE framework effectively captures global features of sentences, addressing limitations of traditional RNNLMs. The model's performance in both quantitative evaluations and qualitative analyses suggests promising avenues for future developments in text generation and understanding.