PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
Language style is necessary for AI systems to understand and generate diverse human language accurately. However, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application needs. To overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking style. Grounded in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of LLMs. For such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized texts. Comprehensive experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information.
Code (2)
Tasks
SentenceStyle TransferText Style TransferMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
PSSTRNet: Progressive Segmentation-guided Scene Text Removal Network
Scene text removal (STR) is a challenging task due to the complex text fonts, colors, sizes, and background textures in scene images. However, most previous methods learn both text location and background inpainting impl…
DecoderSegmentationText SegmentationPareto Self-Supervised Training for Few-Shot Learning
While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data. Exploiting the com…
Auxiliary LearningFew-Shot LearningInductive BiasSelf-Supervised LearningJoint Optimization for Cooperative Image Captioning
When describing images with natural language, descriptions can be made more informative if tuned for downstream tasks. This can be achieved by training two networks: a "speaker" that generates sentences given an image an…
Image CaptioningCooperative image captioning
When describing images with natural language, the descriptions can be made more informative if tuned using downstream tasks. This is often achieved by training two networks: a "speaker network" that generates sentences g…
Image CaptioningThe Post-Stroke Speech Transcription (PSST) Challenge
We present the outcome of the Post-Stroke Speech Transcription (PSST) challenge. For the challenge, we prepared a new data resource of responses to two confrontation naming tests found in AphasiaBank, extracting audio an…