 To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations ( DIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.
<meaning-changed>  To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations ( DIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.
 To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations ( CTIR ). Specifically, we allow the model to predict unseen intents in the trainingstage , with the corresponding label names serving as input utterances.