Disentangled Representation Learning for Non-Parallel Text Style Transfer (1808.04339v2)

Abstract: This paper tackles the problem of disentangling the latent variables of style and content in LLMs. We propose a simple yet effective approach, which incorporates auxiliary multi-task and adversarial objectives, for label prediction and bag-of-words prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space. This disentangled latent representation learning method is applied to style transfer on non-parallel corpora. We achieve substantially better results in terms of transfer accuracy, content preservation and language fluency, in comparison to previous state-of-the-art approaches.

• The paper introduces a novel autoencoder model that separates style and content using multi-task and adversarial training.
• The method leverages bag-of-words approximation for content retention, achieving superior performance on Yelp and Amazon review datasets.
• The approach enables flexible and robust non-parallel text style transfer, opening new pathways for advanced NLP applications.

Disentangled Representation Learning for Non-Parallel Text Style Transfer

The paper "Disentangled Representation Learning for Non-Parallel Text Style Transfer" by Vineet John et al. introduces an innovative approach to disentangle style and content in the latent space of neural networks for text generation, a task of significant interest within the field of NLP. This work focuses on non-parallel text style transfer—a challenging problem due to the absence of sentence pairs with matching content but different styles for training.

Core Contribution

The authors propose a method grounded in an autoencoder framework, which segments the latent space into two distinct subspaces: one for style and another for content. Particularly intriguing is the use of multi-task learning combined with adversarial objectives to achieve and verify this segmentation. Multi-task objectives are employed to ensure the style information is captured effectively in the style space, using auxiliary loss functions for style label prediction. Conversely, adversarial objectives are utilized to discourage predictability of style information from the content space, thereby encouraging a clean separation between style and content.

Approach Details

The autoencoding architecture is a sequence-to-sequence model leveraging a recurrent neural network (RNN) to encode and decode sentences. The key innovation lies in augmenting this model with disentanglement mechanisms—a concept somewhat mature in image processing but novel in NLP.

1. Style and Content Separation:
   • Multi-task learning predicts style from a designated vector representing style features, reinforcing correct style attribution.
   • Adversarial training enhances the model’s ability to obscure style information within the content vector, thus improving the learning of invariant content representations.
2. Content Approximation with BoW:
   • The authors extend the idea of disentanglement by approximating content through bag-of-words (BoW) features focusing on style-neutral elements. This addresses the often vague boundary between style and content, facilitating more accurate content retention.
3. Training and Inference:
   • The model is trained on non-parallel, style-labeled corpora, where conventional autoencoding losses are enhanced by auxiliary multi-task and adversarial losses. Inference involves encoding the content of an input sentence while inferring a novel style vector, generating style-transferred text with preserved content.

Results

Empirical evaluations validate the proposed model's effectiveness on two datasets—Yelp and Amazon reviews—demonstrating superior performance relative to previous state-of-the-art models in terms of style-transfer accuracy, content preservation, and language fluency. The authors conduct both qualitative and quantitative analyses, showing that the disentangled spaces indeed exhibit the intended separation. The proposed methods achieve performance improvements by a substantial margin, underscoring both the robustness and applicability of the approach.

Implications and Future Directions

The implications of this research extend to various NLP applications where style transfer and content integrity are crucial—ranging from automated dialogue systems to content personalization. The authors’ approach opens pathways for further explorations in the disentangled representation paradigm, potentially driving advancements in both theoretical understanding and practical implementations in unsupervised LLM training.

Speculatively, future research may explore the potential integration with more diverse style dimensions beyond sentiment, incorporate more refined architectures such as transformer-based models, and extend the approach to multilingual settings. Moreover, augmenting such models with pre-trained LLMs might further enhance the quality of disentangled representations and text generation tasks.

Overall, this paper represents a step forward in disentangled representation learning within the NLP community, offering a robust framework that can be expanded upon to address various complex and nuanced text processing tasks.