Joint-Average Mean and Variance Feature Matching (JAMVFM) Semi-supervised GAN with Additional-Objective Training Function for Intent Detection

Intent detection, a crucial task in spoken language understanding (SLU) systems, often faces challenges due to the requirement for extensive labeled training data. However, the process of collecting such data is both resource-intensive and time-consuming. To mitigate these challenges, leveraging Semi-Supervised Generative Adversarial Networks (SS-GANs) presents a promising strategy. By employing SS-GANs, it becomes possible to fine-tune pre-trained transformer models like BERT using unlabeled data, thereby improving intent detection performance without the need for extensive labeled datasets. This article introduces a novel approach called Joint-Average Mean and Variance Feature Matching GAN (JAMVFM-GAN) with the additional objective to improve SS-GAN learning. By incorporating information about both mean and variance during latent feature learning, JAMVFM-GAN aims to more accurately capture the underlying data manifold. Except JAMVFM, we proposed an additional loss function for discriminator to enhance its discriminative capabilities. Experimental results demonstrate that JAMVFM-GAN along with additional objective function outperforms traditional SS-GAN in Intent Detection tasks. The results indicate the maximum relative improvement of 3.84%, 3.85%, and 1.04% over the baseline on the ATIS, SLURP, and SNIPS datasets, respectively.