Zero-shot intent detection (ZSID) aims to deal with the continuously emerging
intents without annotated training data. However, existing ZSID systems suffer
from two limitations: 1) They are not good at modeling the relationship between
seen and unseen intents. 2) They cannot effectively recognize unseen intents
under the generalized intent detection (GZSID) setting. A critical problem
behind these limitations is that the representations of unseen intents cannot
be learned in the training stage. To address this problem, we propose a novel
framework that utilizes unseen class labels to learn Class-Transductive Intent
Representations (CTIR). Specifically, we allow the model to predict unseen
intents during training, with the corresponding label names serving as input
utterances. On this basis, we introduce a multi-task learning objective, which
encourages the model to learn the distinctions among intents, and a similarity
scorer, which estimates the connections among intents more accurately. CTIR is
easy to implement and can be integrated with existing methods. Experiments on
two real-world datasets show that CTIR brings considerable improvement to the
baseline systems.

本研究提出了一种利用未知类标签学习类转导意图表示（CTIR）的框架，该框架采用多任务学习目标和相似度评分器来改善现有的零样本意图检测系统不能识别未知意图的问题，并在两个真实数据集上获得了相当的性能提升。