  This paper presents a Soft Labeling and Noisy Mixup-based open intent
classification model (SNOiC). Most of the previous works have used
threshold-based methods to identify open intents, which are prone to
overfitting and may produce biased predictions. Additionally, the need for more
available data for an open intent class presents another limitation for these
existing models. SNOiC combines Soft Labeling and Noisy Mixup strategies to
reduce the biasing and generate pseudo-data for open intent class. The
experimental results on four benchmark datasets show that the SNOiC model
achieves a minimum and maximum performance of 68.72\% and 94.71\%,
respectively, in identifying open intents. Moreover, compared to
state-of-the-art models, the SNOiC model improves the performance of
identifying open intents by 0.93\% (minimum) and 12.76\% (maximum). The model's
efficacy is further established by analyzing various parameters used in the
proposed model. An ablation study is also conducted, which involves creating
three model variants to validate the effectiveness of the SNOiC model.
