Distance Sampling-based Paraphraser Leveraging ChatGPT for Text Data Manipulation
<|reference_start|>Distance Sampling-based Paraphraser Leveraging ChatGPT for Text Data Manipulation: 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.<|reference_end|>
@article{oh2024distance,
    title={Distance Sampling-based Paraphraser Leveraging ChatGPT for Text Data
  Manipulation},
    author={Yoori Oh, Yoseob Han, Kyogu Lee},
    journal={arXiv preprint arXiv:2405.00367},
    year={2024},
    doi={10.1145/3626772.3657976},
    archivePrefix={arXiv},
    eprint={2405.00367},
    primaryClass={cs.IR cs.AI cs.SD eess.AS}
}