Self-supervised learning models have revolutionized the field of speech
processing. However, the process of fine-tuning these models on downstream
tasks requires substantial computational resources, particularly when dealing
with multiple speech-processing tasks. In this paper, we explore the potential
of adapter-based fine-tuning in developing a unified model capable of
effectively handling multiple spoken language processing tasks. The tasks we
investigate are Automatic Speech Recognition, Phoneme Recognition, Intent
Classification, Slot Filling, and Spoken Emotion Recognition. We validate our
approach through a series of experiments on the SUPERB benchmark, and our
results indicate that adapter-based fine-tuning enables a single
encoder-decoder model to perform multiple speech processing tasks with an
average improvement of 18.4% across the five target tasks while staying
efficient in terms of parameter updates.

通过适配器微调，我们研究了开展统一模型以有效处理多个口语处理任务的潜力，并在 SUPERB 基准测试中实验证明，适配器微调使得单一的编码 - 解码模型在五个目标任务上的性能提高了 18.4％，同时在参数更新方面保持高效。

基于适配器的多种口语语言处理任务统一模型

An Adapter-Based Unified Model for Multiple Spoken Language Processing  Tasks

Current state-of-the-art results in computer vision depend in part on
fine-tuning large pre-trained vision models. However, with the exponential
growth of model sizes, the conventional full fine-tuning, which needs to store
a individual network copy for each tasks, leads to increasingly huge storage
and transmission overhead. Adapter-based Parameter-Efficient Tuning (PET)
methods address this challenge by tuning lightweight adapters inserted into the
frozen pre-trained models. In this paper, we investigate how to make adapters
even more efficient, reaching a new minimum size required to store a
task-specific fine-tuned network. Inspired by the observation that the
parameters of adapters converge at flat local minima, we find that adapters are
resistant to noise in parameter space, which means they are also resistant to
low numerical precision. To train low-precision adapters, we propose a
computational-efficient quantization method which minimizes the quantization
error. Through extensive experiments, we find that low-precision adapters
exhibit minimal performance degradation, and even 1-bit precision is sufficient
for adapters. The experimental results demonstrate that 1-bit adapters
outperform all other PET methods on both the VTAB-1K benchmark and few-shot
FGVC tasks, while requiring the smallest storage size. Our findings show, for
the first time, the significant potential of quantization techniques in PET,
providing a general solution to enhance the parameter efficiency of
adapter-based PET methods. Code: this https URL

透過將輕量級適配器插入凍結的預訓練模型並使用低精度量化方法以減少存儲空間，本研究發現低精度的適配器達到與高精度適配器相當的性能，且 1 位精度就足夠。