  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.
