# Effective Slogan Generation with Noise Perturbation

arXiv: https://arxiv.org/abs/2310.04472 · HTML: https://mlsanity.com/paper/2310.04472/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [FreeLB: Enhanced Adversarial Training for Natural Language Understanding](https://mlsanity.com/paper/1909.11764/) (2019)
- [Better Fine-Tuning by Reducing Representational Collapse](https://mlsanity.com/paper/2008.03156/) (2020)
- [Step-unrolled Denoising Autoencoders for Text Generation](https://mlsanity.com/paper/2112.06749/) (2021)
- [Smoothed Contrastive Learning for Unsupervised Sentence Embedding](https://mlsanity.com/paper/2109.04321/) (2021)
- [Word Embedding Perturbation for Sentence Classification](https://mlsanity.com/paper/1804.08166/) (2018)
- [Sharpness-Aware Minimization with Dynamic Reweighting](https://mlsanity.com/paper/2112.08772/) (2021)
- [FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness](https://mlsanity.com/paper/2211.00294/) (2022)
- [Slogatron: Advanced Wealthiness Generator](https://mlsanity.com/paper/1809.09563/) (2018)

## Built on by (most cited first, 2 of 2)

- [Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability](https://mlsanity.com/paper/2506.15629/) (2025)
- [Recontextualizing Famous Quotes for Brand Slogan Generation](https://mlsanity.com/paper/2602.06049/) (2026)

## Abstract (arXiv)

Slogans play a crucial role in building the brand's identity of the firm. A slogan is expected to reflect firm's vision and brand's value propositions in memorable and likeable ways. Automating the generation of slogans with such characteristics is challenging. Previous studies developted and tested slogan generation with syntactic control and summarization models which are not capable of generating distinctive slogans. We introduce a a novel apporach that leverages pre-trained transformer T5 model with noise perturbation on newly proposed 1:N matching pair dataset. This approach serves as a contributing fator in generting distinctive and coherent slogans. Turthermore, the proposed approach incorporates descriptions about the firm and brand into the generation of slogans. We evaluate generated slogans based on ROUGE1, ROUGEL and Cosine Similarity metrics and also assess them with human subjects in terms of slogan's distinctiveness, coherence, and fluency. The results demonstrate that our approach yields better performance than baseline models and other transformer-based models.