基于距离采样的改写器利用ChatGPT进行文本数据操纵
Distance Sampling-based Paraphraser Leveraging ChatGPT for Text Data Manipulation
AI总结：
本文针对音频-文本检索中因字幕重复导致的数据不平衡问题，提出利用ChatGPT结合距离采样与Jaccard相似度文本簇的少样本提示方法，可控生成多样化改写文本，显著提升检索性能并优于传统文本增强技术。
摘要：
音频-语言检索研究日益受到关注，其目标是建立音频与文本模态之间的关联。然而，与音频样本相比，大多数音频-文本配对数据集中的文本数据往往缺乏丰富的表达。音频-文本数据集面临的一个重大挑战是：尽管音频样本不同，却存在相似或相同的字幕。因此，在多对一映射条件下，音频-文本数据集会导致检索任务性能不佳。本文提出了一种新颖的方法来解决音频-语言检索任务中的数据不平衡问题。为克服这一局限，我们引入了一种基于距离采样的改写器方法，该方法利用ChatGPT，通过距离函数生成可控分布的操纵文本数据。对于一组具有相同上下文的句子，距离用于计算任意两个句子的操纵程度，并利用由Jaccard相似度定义的具有相似距离的文本簇对ChatGPT进行少样本提示。因此，当ChatGPT应用于基于文本簇的少样本提示时，可以根据距离调整操纵文本的多样性。实验表明，所提方法显著提升了音频-文本检索的性能，优于传统的文本增强技术。
英文摘要：
There has been growing interest in audio-language retrieval research, where the objective is to establish the correlation between audio and text modalities. However, most audio-text paired datasets often lack rich expression of the text data compared to the audio samples. One of the significant challenges facing audio-text datasets is the presence of similar or identical captions despite different audio samples. Therefore, under many-to-one mapping conditions, audio-text datasets lead to poor performance of retrieval tasks. In this paper, we propose a novel approach to tackle the data imbalance problem in audio-language retrieval task. To overcome the limitation, we introduce a method that employs a distance sampling-based paraphraser leveraging ChatGPT, utilizing distance function to generate a controllable distribution of manipulated text data. For a set of sentences with the same context, the distance is used to calculate a degree of manipulation for any two sentences, and ChatGPT's few-shot prompting is performed using a text cluster with a similar distance defined by the Jaccard similarity. Therefore, ChatGPT, when applied to few-shot prompting with text clusters, can adjust the diversity of the manipulated text based on the distance. The proposed approach is shown to significantly enhance performance in audio-text retrieval, outperforming conventional text augmentation techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
