Effective Slogan Generation with Noise Perturbation

• URL: http://arxiv.org/abs/2310.04472v2
• Date: Thu, 12 Oct 2023 15:57:04 GMT
• Title: Effective Slogan Generation with Noise Perturbation
• Abstract summary: We introduce a novel apporach that leverages pre-trained transformer T5 model with noise metrics on newly proposed 1:N matching pair dataset. We evaluate generated slogans based on ROUGE1, ROUGEL and Cosine Similarity and also assess them with human subjects in terms of slogan's distinctiveness, coherence, and fluency.
• Score: 2.80888070977859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation [50.411841997631484]
  We present Unison, a unified framework that promotes coherence across the motion, speech, and sound modalities.<n>We show that Unison achieves state-of-the-art performance in both audio perceptual quality and cross-modal synchronization.
  arXiv  Detail & Related papers  (2026-05-09T06:32:54Z)
• Recontextualizing Famous Quotes for Brand Slogan Generation [9.48100252812595]
  We argue that effective slogans should balance novelty with familiarity.<n>We propose a new paradigm that recontextualizes persona-related famous quotes for slogan generation.
  arXiv  Detail & Related papers  (2026-01-12T04:56:48Z)
• On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation [88.77441715819366]
  Generative spoken language models pretrained on large-scale raw audio can continue a speech prompt with appropriate content.<n>We propose a variety of likelihood- and generative-based evaluation methods that serve in place of naive global token perplexity.
  arXiv  Detail & Related papers  (2026-01-09T22:01:56Z)
• From Unlearning to UNBRANDING: A Benchmark for Trademark-Safe Text-to-Image Generation [0.7798283447125206]
  Brand recognition is multi-dimensional, extending beyond explicit logos to encompass distinctive structural features.<n>We introduce unbranding, a novel task for the fine-grained removal of both trademarks and subtle structural brand features.<n>Our results, validated by our Vision Language Models metric, confirm unbranding is a distinct, practically relevant problem.
  arXiv  Detail & Related papers  (2025-12-15T23:15:36Z)
• MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production [93.32354378820648]
  We propose a unified framework for continuous sign language production, easing communication between sign and non-sign language users. A sequence diffusion model, utilizing embeddings extracted from text or speech, is crafted to generate sign predictions step by step. Experiments on How2Sign and PHOENIX14T datasets demonstrate that our model achieves competitive performance in sign language production.
  arXiv  Detail & Related papers  (2024-07-04T13:53:50Z)
• COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport [25.73474734479759]
  This research paper introduces a novel framework based on contrastive optimal transport. It effectively addresses the challenges of maintaining target interaction and promoting diversification in generating counter-narratives. Our proposed model significantly outperforms current methods evaluated by metrics from multiple aspects.
  arXiv  Detail & Related papers  (2024-06-18T06:24:26Z)
• Multimodal Large Language Model is a Human-Aligned Annotator for Text-to-Image Generation [87.50120181861362]
  VisionPrefer is a high-quality and fine-grained preference dataset that captures multiple preference aspects. We train a reward model VP-Score over VisionPrefer to guide the training of text-to-image generative models and the preference prediction accuracy of VP-Score is comparable to human annotators.
  arXiv  Detail & Related papers  (2024-04-23T14:53:15Z)
• Diversified in-domain synthesis with efficient fine-tuning for few-shot classification [64.86872227580866]
  Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. We propose DISEF, a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few-shot classification.
  arXiv  Detail & Related papers  (2023-12-05T17:18:09Z)
• JAB: Joint Adversarial Prompting and Belief Augmentation [81.39548637776365]
  We introduce a joint framework in which we probe and improve the robustness of a black-box target model via adversarial prompting and belief augmentation. This framework utilizes an automated red teaming approach to probe the target model, along with a belief augmenter to generate instructions for the target model to improve its robustness to those adversarial probes.
  arXiv  Detail & Related papers  (2023-11-16T00:35:54Z)
• Empirical Study of Zero-Shot NER with ChatGPT [19.534329209433626]
  Large language models (LLMs) exhibited powerful capability in various natural language processing tasks. This work focuses on exploring LLM performance on zero-shot information extraction. Inspired by the remarkable reasoning capability of LLM on symbolic and arithmetic reasoning, we adapt the prevalent reasoning methods to NER.
  arXiv  Detail & Related papers  (2023-10-16T03:40:03Z)
• Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations [59.10748929158525]
  Abstract Representations (AMR) can greatly improve the performance of unsupervised syntactically controlled paraphrase generation. Our proposed model, AMR-enhanced Paraphrase Generator (AMRPG), encodes the AMR graph and the constituency parses the input sentence into two disentangled semantic and syntactic embeddings. Experiments show that AMRPG generates more accurate syntactically controlled paraphrases, both quantitatively and qualitatively, compared to the existing unsupervised approaches.
  arXiv  Detail & Related papers  (2022-11-02T04:58:38Z)
• RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models [32.806292167848156]
  We propose RockNER to audit the robustness of named entity recognition models. We replace target entities with other entities of the same semantic class in Wikidata. At the context level, we use pre-trained language models to generate word substitutions.
  arXiv  Detail & Related papers  (2021-09-12T21:30:21Z)
• Improving Generation and Evaluation of Visual Stories via Semantic Consistency [72.00815192668193]
  Given a series of natural language captions, an agent must generate a sequence of images that correspond to the captions. Prior work has introduced recurrent generative models which outperform synthesis text-to-image models on this task. We present a number of improvements to prior modeling approaches, including the addition of a dual learning framework.
  arXiv  Detail & Related papers  (2021-05-20T20:42:42Z)
• Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation [24.940159321142165]
  We propose to promote the generation diversity of the neural dialogue models via soft embedding augmentation. New embeddings serve as the input of the model to replace the original one. Our experimental results on two datasets illustrate that our proposed method is capable of generating more diverse responses than raw models.
  arXiv  Detail & Related papers  (2021-03-02T07:28:56Z)
• Generating Coherent and Diverse Slogans with Sequence-to-Sequence Transformer [0.1835211348413763]
  We propose a sequence-to-sequence transformer model to generate slogans from a brief company description. A naive sequence-to-sequence model fine-tuned for slogan generation is prone to introducing false information. We use delexicalisation to address this problem and improve the generated slogans' quality by a large margin.
  arXiv  Detail & Related papers  (2021-02-11T10:25:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.