Discrete Autoencoders for Sequence Models (1801.09797v1)

Abstract: Recurrent models for sequences have been recently successful at many tasks, especially for LLMing and machine translation. Nevertheless, it remains challenging to extract good representations from these models. For instance, even though language has a clear hierarchical structure going from characters through words to sentences, it is not apparent in current LLMs. We propose to improve the representation in sequence models by augmenting current approaches with an autoencoder that is forced to compress the sequence through an intermediate discrete latent space. In order to propagate gradients though this discrete representation we introduce an improved semantic hashing technique. We show that this technique performs well on a newly proposed quantitative efficiency measure. We also analyze latent codes produced by the model showing how they correspond to words and phrases. Finally, we present an application of the autoencoder-augmented model to generating diverse translations.

• The paper introduces a novel discretization technique using semantic hashing with a saturating sigmoid and straight-through gradients to stabilize training.
• The proposed method outperforms Gumbel-Softmax, achieving up to 59% DSAE in character-level language modeling and enhanced performance in translation tasks.
• The work demonstrates that integrating discrete latent codes yields interpretable linguistic units and offers improved diversity through mixed sample-beam decoding.

Discrete Autoencoders for Sequence Models

The paper "Discrete Autoencoders for Sequence Models" by Kaiser and Bengio addresses the integration of discrete latent representations into sequence models, aiming to improve the representation of hierarchical structures inherent in language. This work enhances the autoencoder framework by incorporating an improved semantic hashing technique, overcoming challenges associated with gradient propagation through discrete variables.

Main Contributions

1. Discretization Technique: The authors propose a novel discretization method drawing on semantic hashing. This method uses a saturating sigmoid function combined with a straight-through gradient pass, eliminating the need for annealing noise or additional tuning of loss factors. This approach ensures stable discretization without necessitating complex adjustments.
2. Performance Evaluation: A quantitative efficiency measure is introduced, termed Discrete Sequence Autoencoding Efficiency (DSAE), which evaluates the utility of discrete bits in reducing perplexity in sequence prediction tasks. Results demonstrate that the proposed method outperforms the Gumbel-Softmax technique, achieving higher DSAE across several tasks, such as character-level LLMing and translation.
3. Application in LLMs: The paper illustrates the utility of the discrete autoencoder in enhancing LLMs by showing that the latent codes correspond to interpretable linguistic units, such as words and phrases. Additionally, an innovative mixed sample-beam decoding approach is presented, offering improved diversity in model outputs without compromising semantic integrity.

Experimental Results

The experimental section showcases the method's efficacy by applying it to various sequence tasks, including character-level and word-level LLMs, as well as translation tasks. The proposed method achieves DSAE values of up to 59% on character-level LLMs and 55% on word-level tasks, significantly outperforming Gumbel-Softmax in these settings.

Theoretical and Practical Implications

The incorporation of discrete latent spaces into autoencoder architectures presents promising advancements for hierarchical sequence modeling. While traditional autoregressive models represent text at a continuous level, this paper argues for the advantages of discrete representations in capturing the structure of natural language more effectively.

Practically, this approach lends itself to various applications, including:

• Neural Machine Translation: By enabling the generation of diverse translations through mixed decoding methods, it's possible to produce outputs with varied syntax but preserved semantics, a critical enhancement over traditional beam search strategies.
• Reinforcement Learning: The latent code can potentially be exploited for higher-level planning by encoding sequences into discrete actions. Such methodologies could improve exploration strategies and decision-making processes in complex environments.

Future Directions

The paper opens several avenues for further research:

• Architecture Optimization: The exploration of different architectures for the autoencoding function could further refine the model's efficiency and interpretability of latent codes.
• Generative Models: Extending this framework to train multi-scale generative models could lead toward generating more realistic images, audio, and video by leveraging discrete latent representations.
• Scalability and Generalization: Research into how these methods generalize across diverse datasets and larger-scale problems will be crucial for broader applicability.

In summary, this paper contributes a robust framework for discrete autoencoders in sequence models, providing both theoretical insights and practical tools for improving sequence representation and diversity in generated outputs.