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Fine-grained Text Style Transfer with Diffusion-Based Language Models

Yiwei Lyu, Tiange Luo, Jiacheng Shi, Todd C. Hollon, Honglak Lee

arXiv:2305.19512v2cs.CLcs.AIcs.LG

TL;DR

Whether diffusion-based language models can perform fine-grained text style transfer is relevant because the task calls for more targeted control of generated text. This paper trains a diffusion-based model on StylePTB without external knowledge or pretrained weights and reports state-of-the-art performance on individual and compositional transfers.

  • Problem

    Whether diffusion-based language models can handle fine-grained text style transfer, which requires targeted control over generated text, remains to be examined.

    Source: §1 · pp. 1–2

  • Method

    The paper adapts a diffusion-based language model to StylePTB, training it from scratch with limited data and no external weights or tools.

    Source: §1 · p. 2

  • Results

    The model achieves state-of-the-art performance on StylePTB’s individual transfers and compositions, outperforming prior work including methods using pretrained weights, embeddings, and external grammar parsers.

    Source: §1 · p. 2, §7 · p. 9

  • Takeaways & Limitations

    The authors conclude that diffusion-based language models could be an alternative to consider for controllable text generation under low-resource settings.

    Source: §7 · p. 9

    Only one typical diffusion-based language model was explored, so the conclusions may not generalize to models with discrete state spaces.

    Source: §6 · p. 8

Abstract

from arXiv · show

Diffusion probabilistic models have shown great success in generating high-quality images controllably, and researchers have tried to utilize this controllability into text generation domain. Previous works on diffusion-based language models have shown that they can be trained without external knowledge (such as pre-trained weights) and still achieve stable performance and controllability. In this paper, we trained a diffusion-based model on StylePTB dataset, the standard benchmark for fine-grained text style transfers. The tasks in StylePTB requires much more refined control over the output text compared to tasks evaluated in previous works, and our model was able to achieve state-of-the-art performance on StylePTB on both individual and compositional transfers. Moreover, our model, trained on limited data from StylePTB without external knowledge, outperforms previous works that utilized pretrained weights, embeddings, and external grammar parsers, and this may indicate that diffusion-based language models have great potential under low-resource settings.

1 Introduction

Diffusion models offer controllable generation, but earlier diffusion-language tasks did not modify existing text to express specific fine-grained styles while preserving other information. This paper applies a diffusion-based model to StylePTB using limited data and no external knowledge, reporting strong performance on individual and composed transfers.

1 Introduction

  • Earlier diffusion-language tasks did not modify existing text to express specific stylistic characteristics while preserving the rest of its information.Fine-grained transfer, such as verb emphasis, must change a targeted property without changing other conveyed information.Source: §1 · p. 1
  • The study trains a diffusion-based model on StylePTB without pretrained weights or external grammar parsers, where each of 13 non-lexical transfers has at most a few thousand sentence pairs.The dataset contains 7,719 Penn Treebank sentences and their transferred outputs.Source: §1 · pp. 1–2
  • The model supports both one-model multitasking across transfers and composing multiple fine-grained transfers.The paper presents this as a step toward highly controlled text generation under low-resource conditions.Source: §1 · pp. 2–3
  • The model achieves state-of-the-art performance on StylePTB’s individual and compositional fine-grained transfers, despite using limited data and no external knowledge.The multitask model performs best on 88 of 91 metrics and comes close to human performance on easy and medium tasks.Source: §1 · pp. 2–3

2 Backgrounds

Fine-grained text style transfer targets specific stylistic changes while retaining other aspects, addressing limits of high-level controls and challenging composition. Diffusion models generate by progressively adding noise and learning to reverse that process; language models apply the process in token-embedding space.

2.1 Fine-grained Text Style Transfer and StylePTB

  • Fine-grained text style transfer aims to control targeted stylistic properties more precisely than high-level transfers such as sentiment or formality.StylePTB organizes transfers across four linguistic axes and includes 21 individual transfers, 13 of them non-lexical.Source: §2.1 · p. 3
  • Existing fine-grained transfers remain short of human performance on many tasks, and composing multiple transfers remains challenging.The paper describes fine-grained transfers as a way to add specific stylistic properties to generated text while keeping its content unchanged.Source: §2.1 · p. 3

2.2 Diffusion Probabilistic Models

  • Diffusion models add Gaussian noise to data over forward steps, then train a model to remove noise and recover the original.At inference, generation begins with sampled Gaussian noise and proceeds through denoising.Source: §2.2 · p. 3
  • Diffusion-based language generation performs diffusion and denoising in token-embedding space.The paper’s model builds on DiffuSeq (Gong et al., 2022).Source: §2.2 · p. 3

3 Methodology

The method treats fine-grained style transfer as conditional generation: source text and requested transfer tokens condition a diffusion model that generates target text in embedding space. Training corrupts target embeddings with sampled Gaussian noise, while inference iteratively denoises and decodes them.

3 Methodology

  • The model conditions generation on the source sentence and requested transfer operation or operations, represented by special style tokens prepended to the source.It adapts DiffuSeq and uses a transformer with randomly initialized, jointly optimized diffusion and token-embedding components.Source: §3 · pp. 3–4
  • The diffusion process operates over target-sentence token embeddings, with source embeddings concatenated before the target during the backward process.Both the diffusion transformer and token embeddings are initialized randomly, so the model uses no prior task or English-language knowledge.Source: Figure 1 · p. 4, §3 · p. 4
  • Training samples a diffusion step, adds the corresponding Gaussian noise to target embeddings, and trains the transformer to recover the target representation.The source embeddings are concatenated with the noised target embeddings; the objective is mean squared error.Source: §3 · p. 4
  • At inference, generation starts from randomly initialized target embeddings conditioned on the source and style tokens, then iteratively denoises the target.The final embeddings are mapped to the closest token embeddings by cosine distance and decoded into text.Source: §3 · p. 4

4.1 Dataset

StylePTB supplies paired data for fine-grained single and compositional transfers, with the experiments focusing on 13 non-lexical transfers and two composition datasets. Its limited source-sentence base constrains the amount and diversity of training data.

4.1 Dataset

  • StylePTB pairs sentences before and after 21 fine-grained transfers and includes compositions; experiments use 13 non-lexical transfers plus Tense + Voice and Tense + PP Removal compositions.The composition datasets cover valid combinations of zero, one, or two transfers.Source: §4.1 · p. 4
  • StylePTB derives from 7,719 Penn Tree Bank sentences and their stylistic variations, limiting training-data volume and diversity.This makes learning linguistic knowledge without external knowledge or pretrained weights more challenging for DiffuSeq.Source: §4.1 · p. 4
  • For fair comparison, preprocessing replaces numbers with NUM and words occurring fewer than three times with UNK, using the prior works’ 0.9/0.05/0.05 train/validation/test splits.The splits match those used by previous works.Source: §4.1 · p. 5

4.2 Evaluation Metrics

Evaluation follows the methods of Lyu et al. (2021), using seven nlg-eval metrics to compare transferred outputs with StylePTB target sentences.

4.2 Evaluation Metrics

  • Evaluation methods follow Lyu et al. (2021).The reported scores use the nlg-eval package.Source: §4.2 · p. 5
  • The seven reported metrics are BLEU 1–4, METEOR, ROUGE-L, and CiDER.Source: §4.2 · p. 5
  • Scores compare each generated transferred sentence with its dataset ground-truth target sentence.Source: §4.2 · p. 5

4.3 Single style transfer experiment

The single-transfer experiments compare diffusion models against prior systems and human performance. The multitask model leads on easy and medium transfers and most hard-transfer metrics, while outperforming separate diffusion models across all transfers.

4.3.1 Baselines

  • Baselines span GPT-2, Seq2Seq, RetrieveEdit, Steering Vector, TAILOR, and Neural QCFG variants; some use pretrained models, GloVe embeddings, or external grammar parsers.Source: §4.3.1 · p. 5

4.3.2 Results and Analysis

  • DiffuSeq Multitask outperforms transfer-specific DiffuSeq on every transfer despite handling 13 transfers in one model.The authors suggest additional cross-task training data may improve representations and shared grammatical-pattern knowledge.Source: §4.3.2 · p. 5
  • DiffuSeq Multitask performs on par with human annotators on easy and medium transfers.Source: §4.3.2 · p. 5
  • DiffuSeq Multitask leads all easy and medium transfers and most hard-transfer metrics, trailing Neural QCFG + copy slightly on some hard-transfer metrics.The reported comparison covers easy and medium results in Table 2 and hard-transfer results in Table 3.Source: §4.3.2 · p. 5, Table 2 · p. 6, Table 3 · p. 7

4.4 Compositional style transfer experiment

For compositional transfers, one style-token-conditioned DiffuSeq model handles combinations of transfers. It significantly outperforms the baselines on every task and metric.

4.4.1 Baselines

  • The compositional baselines are SeqGPT, which applies fine-tuned GPT-2 models sequentially, and CS-GPT, a modified GPT-2 model conditioned on style tokens.Source: §4.4.1 · p. 7

4.4.2 Results and Analysis

  • One DiffuSeq model uses style tokens to select compositions and significantly outperforms baselines on every compositional task and metric.The model supports compositions of multiple fine-grained transfers.Source: §4.4.2 · p. 7

5 Related Works

Fine-grained text style transfer remains challenging despite strong results on some high-level transfers, and the paper explores diffusion models as a new approach. For composed transfers, DiffuSeq outperformed all three baselines by a large margin; the paper builds on continuous-space diffusion models.

5.1 Automated Text Style Transfer

  • Fine-grained text style transfer remains challenging, although some prior methods perform well on high-level transfers such as sentiment and formality.The paper explores diffusion models as a new approach for fine-grained transfer.Source: §5.1 · pp. 7–8
  • DiffuSeq outperformed all three Lyu et al. baselines by a large margin on compositions of Tense + Voice and Tense + PP Removal transfers.The results are reported in Table 4.Source: Table 4 · p. 8

5.2 Natural language processing with diffusion model

  • The paper builds on continuous-space diffusion models, which denoise a Gaussian noise vector into a sentence, rather than discrete-state approaches.The authors chose this type because it is closer to image diffusion and has shown success on controlled generation tasks.Source: §5.2 · p. 8

6 Limitations and Future works

The evaluation excludes pretrained weights and grammar parsers, and the authors identify limits in model coverage and real low-resource evaluation. They propose testing additional resources, model types, and architectures.

  • The model was evaluated without pretrained weights or grammar parsers, so the authors say it was not tested at its full potential.They propose exploring pretrained weights or word embeddings and training with enough data to assess possible performance gains.Source: §6 · p. 8
  • Because the study explored one typical diffusion-based language model, its conclusions may not generalize to discrete-state models.All experiments used the same architecture; future work could test different architectures and source-conditioning methods such as cross-attention.Source: §6 · p. 8
  • The study did not evaluate actual low-resource settings such as low-resource languages or rare domains, leaving that potential for future work.The authors note that the models worked with limited data and no external knowledge or pretrained weights.Source: §6 · pp. 8–9

7 Conclusions

The DiffuSeq-based model achieves state-of-the-art performance on individual and composed fine-grained transfers, despite using very limited data and no external knowledge or pretrained weights. The authors see diffusion-based language models as a possible option for low-resource controllable generation.

  • The model achieves state-of-the-art performance on all transfers and transfer compositions, outperforming prior StylePTB work including methods using pretrained weights, word embeddings, and external grammar parsers.It is also on par with human performance on many transfers.Source: §7 · p. 9
  • Diffusion-based language models may be worth considering for low-resource controllable text generation, where pretrained models or ample training data may be unavailable.The authors describe this as potential, not as a result from experiments in actual low-resource settings.Source: §7 · p. 9
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