@techreport{oai:ipsj.ixsq.nii.ac.jp:00212810,
 author = {Bo, Wang and Tsunenori, Mine and Bo, Wang and Tsunenori, Mine},
 issue = {4},
 month = {Sep},
 note = {Many approaches have been proposed to detect Out-of-Domain texts in user intent classification, but most of them are trained on In-Domain data which cannot utilize the huge potential of unlabeled data, or need many hard-to-obtain real Out-of-Domain data. Recently, an Out-of-Domain generation framework has been proposed, which overcame the drawbacks of previous work and got better results. However, the current implementation is far from practical because of its common but ineffective network implementation and unconsidered potential conflicts in the GAN training procedure. In this paper, we propose an improved approach to realize a better Out-of-Domain texts generation, where we modify the Autoencoder for faster learning of context data, and also regularize the output to decrease the difficulty of GAN imitation afterwards. For GAN, we utilize different activation scheme and a more moderate training signal to solve the training conflicts. Comprehensive experiments on three datasets and efficiency measurements show the practicality and efficiency of our new approach., Many approaches have been proposed to detect Out-of-Domain texts in user intent classification, but most of them are trained on In-Domain data which cannot utilize the huge potential of unlabeled data, or need many hard-to-obtain real Out-of-Domain data. Recently, an Out-of-Domain generation framework has been proposed, which overcame the drawbacks of previous work and got better results. However, the current implementation is far from practical because of its common but ineffective network implementation and unconsidered potential conflicts in the GAN training procedure. In this paper, we propose an improved approach to realize a better Out-of-Domain texts generation, where we modify the Autoencoder for faster learning of context data, and also regularize the output to decrease the difficulty of GAN imitation afterwards. For GAN, we utilize different activation scheme and a more moderate training signal to solve the training conflicts. Comprehensive experiments on three datasets and efficiency measurements show the practicality and efficiency of our new approach.},
 title = {An Improved Approach to Generation and Detection of Out-of-Domain Texts},
 year = {2021}
}