Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfer (1908.09368v1)

Abstract: Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. In this work we introduce the Generative Style Transformer (GST) - a new approach to rewriting sentences to a target style in the absence of parallel style corpora. GST leverages the power of both, large unsupervised pre-trained LLMs as well as the Transformer. GST is a part of a larger `Delete Retrieve Generate' framework, in which we also propose a novel method of deleting style attributes from the source sentence by exploiting the inner workings of the Transformer. Our models outperform state-of-art systems across 5 datasets on sentiment, gender and political slant transfer. We also propose the use of the GLEU metric as an automatic metric of evaluation of style transfer, which we found to compare better with human ratings than the predominantly used BLEU score.

Insights into Generative Style Transformer for Text Style Transfer

This paper articulates an innovative approach to controlled text style transfer, a nuanced task within the domain of Natural Language Generation (NLG). The proposed methodology, termed the Generative Style Transformer (GST), enhances the 'Delete, Retrieve, Generate' (DRG) framework to enable style conversion without relying on parallel style corpora. This paper presents a meticulous advancement in understanding and applying Transformers for text manipulation tasks, marking a significant step forward in this research domain.

The core contribution of this paper is the GST model, which synthesizes the capabilities of large pre-trained LLMs with the structural efficacy of Transformers. The key innovation lies in leveraging Transformers not only for their generative capabilities but also in utilizing attention mechanisms for identifying and manipulating stylistic attributes. The paper demonstrates superior performance across various datasets, including those concerning sentiment, gender, and political slant.

Methodology

The authors provide a detailed exploration of their enhanced DRG framework, which incorporates:

• Delete Component: This component employs a novel Delete Transformer (DT) leveraging BERT-like architectures to identify and isolate stylistic attributes via attention mechanisms. The removal mechanism is optimized through attention weights analysis, offering a nuanced approach to attribute extraction.
• Retrieve Component: The system retrieves stylistic attributes from a corpus using TF-IDF vector similarity, enhancing the compatibility of style attributes with sentence content.
• Generate Component: The GST, a multi-layer decoder-only Transformer inspired by GPT, is fine-tuned to generate sentences in a target style while maintaining content integrity. The paper distinguishes between two GST variants: Blind GST (B-GST) and Guided GST (G-GST), each optimized for different scenarios of style transfer.

Numerical Findings and Evaluation

The model demonstrates notable improvements in both human and automatic evaluation metrics over current state-of-the-art systems. For content and fluency retention, human evaluations strongly favor GST, particularly the B-GST variant. This is corroborated by automatic evaluation metrics such as BLEU and GLEU scores, with the latter emerging as a promising metric due to its balanced consideration of style change and content preservation.

The evaluation also highlights the inadequacies of traditional metrics, such as BLEU and target style accuracy, when used in isolation for assessing style transfer tasks. The introduction of GLEU as a more reliable metric indicates the paper's forward-thinking approach to assessing textual transformations.

Theoretical and Practical Implications

The implementation of GST highlights significant theoretical implications for style transfer research. The ability to disentangle style from content with precision points toward broader applications in privacy preservation, dialogue systems, and creative text generation. Practically, GST promises enhanced flexibility and controllability in style transformation, which are indispensable for real-world applications where style often interacts with functional text aspects.

Speculations and Future Directions

This research opens pathways for further exploration into refining retrieval mechanisms to augment control over targeted style attributes more precisely. Additionally, the successful implementation of G-GST suggests a potential focus on user-controlled style transfer applications, offering broader customization options in personal and commercial AI text applications.

The paper serves as a reference point for future studies aiming to upgrade the robustness and accuracy of style transfer systems, encouraging the application of sophisticated attentional models to nuanced NLG tasks. As future research delves deeper into these methodologies, the community can expect significant advancements in how AI models understand and manipulate the intricate layers of linguistic style.