Prompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model

Kai-Wei Chang; Ming-Hsin Chen; Yun-Ping Lin; Jing Neng Hsu; Paul Kuo-Ming Huang; Chien-yu Huang; Shang-Wen Li; Hung-yi Lee

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arXiv · 2310.02971

Prompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model

Kai-Wei Chang·Ming-Hsin Chen·Yun-Ping Lin·Jing Neng Hsu·Paul Kuo-Ming Huang·Chien-yu Huang·Shang-Wen Li·Hung-yi Lee

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Abstract

Prompting and adapter tuning have emerged as efficient alternatives to fine-tuning (FT) methods. However, existing studies on speech prompting focused on classification tasks and failed on more complex sequence generation tasks. Besides, adapter tuning is primarily applied with a focus on encoder-only self-supervised models. Our experiments show that prompting on Wav2Seq, a self-supervised encoder-decoder model, surpasses previous works in sequence generation tasks. It achieves a remarkable 53% relative improvement in word error rate for ASR and a 27% in F1 score for slot filling. Additionally, prompting competes with the FT method in the low-resource scenario. Moreover, we show the transferability of prompting and adapter tuning on Wav2Seq in cross-lingual ASR. When limited trainable parameters are involved, prompting and adapter tuning consistently outperform conventional FT across 7 languages. Notably, in the low-resource scenario, prompting consistently outperforms adapter tuning.

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BibTeX RIS

Kai-Wei Chang, Ming-Hsin Chen, Yun-Ping Lin, Jing Neng Hsu, Paul Kuo-Ming Huang, Chien-yu Huang, Shang-Wen Li, Hung-yi Lee. 2023-11-14. Prompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model. https://arxiv.org/abs/2310.02971

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