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Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation

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arxiv 1905.05621 v3 pith:U3QJHFZE submitted 2019-05-14 cs.CL

Ning Dai , Jianze Liang , Xipeng Qiu , Xuanjing Huang This is my paper · ORCID
classification cs.CL
keywords stylelatentcontentrepresentationtransfertransformerbetterneural
verification ladder T0 review T1 audit T2 compute T3 formal
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Disentangling the content and style in the latent space is prevalent in unpaired text style transfer. However, two major issues exist in most of the current neural models. 1) It is difficult to completely strip the style information from the semantics for a sentence. 2) The recurrent neural network (RNN) based encoder and decoder, mediated by the latent representation, cannot well deal with the issue of the long-term dependency, resulting in poor preservation of non-stylistic semantic content. In this paper, we propose the Style Transformer, which makes no assumption about the latent representation of source sentence and equips the power of attention mechanism in Transformer to achieve better style transfer and better content preservation.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models

    cs.CL 2025-01 reject novelty 6.0 of 10

    On a Queneau-Fénéon corpus, topic variation increases embedding dispersion more than style variation, but the style effect is confounded with human versus GPT-4o authorship.

  2. StyleRWKV: High-Quality and High-Efficiency Style Transfer with RWKV-like Architecture

    cs.CV 2024-12 conditional novelty 6.0 of 10

    StyleRWKV applies recurrent RWKV-style attention with deformable shifting and skip scanning to achieve fast, high-quality arbitrary style transfer.

  3. Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only

    cs.CL 2025-07 reject novelty 5.0 of 10

    Babel detects and repairs stylistic mismatches in machine translation outputs using a style detector and a diffusion-based applicator trained on monolingual corpora.

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