Enhancing Audio-Text Retrieval Performance through Distance Sampling-based Paraphrasing with ChatGPT

Conceitos Básicos

A novel distance sampling-based paraphraser leveraging ChatGPT can effectively generate manipulated text data to improve performance in audio-text retrieval tasks.

Resumo

The paper proposes a novel distance sampling-based paraphraser that leverages ChatGPT to generate manipulated text data for audio-text retrieval tasks. The key insights are:

Audio-text datasets often suffer from a "many-to-one" mapping problem, where distinct audio samples are associated with the same or similar captions. This can adversely impact the performance of contrastive learning-based audio-text retrieval models.

The proposed paraphraser uses distance metrics like Levenshtein distance or Jaccard similarity to compute the distance between ground-truth sentences and candidate paraphrased sentences. It then employs a few-shot prompting scheme with ChatGPT to generate manipulated text samples that satisfy the desired distance constraints.

By controlling the degree of text manipulation through the distance constraints, the paraphraser can generate a diverse set of text samples for each audio, alleviating the many-to-one mapping issue.

Experiments on the AudioCaps dataset show that the proposed approach outperforms conventional text augmentation techniques and achieves state-of-the-art performance on audio-text retrieval tasks.

The paper also provides insights into the optimal number of few-shot samples and distance constraints for effective text manipulation using the ChatGPT-based paraphraser.

Estatísticas

Many distinct audio samples are often mapped to the same or similar captions in audio-text datasets.
The proposed paraphraser can generate manipulated text samples with varying degrees of distance from the ground-truth captions.
Experiments show that the proposed approach outperforms conventional text augmentation techniques on audio-text retrieval tasks.

Citações

"To overcome the many-to-one mapping occurring in the audio-language domain, we propose a novel distance sampling-based paraphraser that uses metrics to compute the distance between two sentences, such as Levenshtein distance or Jaccard similarity."
"By controlling the degree of text manipulation through the distance constraints, the paraphraser can generate a diverse set of text samples for each audio, alleviating the many-to-one mapping issue."

Principais Insights Extraídos De

Distance Sampling-based Paraphraser Leveraging ChatGPT for Text Data Manipulation

by Yoori Oh,Yos... às arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00367.pdf

Perguntas Mais Profundas

How can the proposed distance sampling-based paraphraser be extended to other multimodal tasks beyond audio-text retrieval, such as image-text or video-text retrieval

The proposed distance sampling-based paraphraser can be extended to other multimodal tasks beyond audio-text retrieval by adapting the distance calculation and manipulation techniques to suit the specific characteristics of different modalities. For image-text retrieval, the paraphraser can utilize visual encoders to extract features from images and incorporate them into the distance calculation process. Similarly, for video-text retrieval, the paraphraser can consider temporal features and motion information in addition to visual and textual content. By adjusting the distance metrics and clustering methods based on the unique properties of each modality, the paraphraser can effectively generate diverse and relevant text samples for different multimodal tasks.

What are the potential limitations of the ChatGPT-based paraphrasing approach, and how can they be addressed to further improve the quality and diversity of the generated text samples

One potential limitation of the ChatGPT-based paraphrasing approach is the reliance on the pre-trained language model's existing knowledge and biases, which may restrict the diversity and quality of the generated text samples. To address this limitation, fine-tuning the ChatGPT model on domain-specific data related to the multimodal task at hand can help tailor the paraphraser to produce more relevant and varied outputs. Additionally, incorporating human feedback and validation mechanisms to assess the quality of the paraphrased text can further enhance the overall performance of the paraphraser. Moreover, integrating techniques like adversarial training or diversity-promoting objectives during paraphrasing can encourage the generation of more diverse and realistic text samples.

Could the distance-based text manipulation technique be combined with other data augmentation methods, such as back-translation or synonym replacement, to create even more diverse and effective training data for multimodal learning

The distance-based text manipulation technique can be combined with other data augmentation methods, such as back-translation or synonym replacement, to create even more diverse and effective training data for multimodal learning. By integrating back-translation, the paraphraser can generate paraphrased text in multiple languages, enhancing the multilinguality of the training data. Synonym replacement can be used to introduce lexical variations and expand the vocabulary of the text samples. Combining these techniques with distance-based manipulation can provide a comprehensive approach to generating augmented training data that is both diverse and contextually relevant, improving the performance of multimodal learning models.