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 , when the label names are given in the form of raw phrases or sentences . 2) They cannot effectively recognize unseen intents under the generalized intent detection (GZSID) setting. A critical factor behind these limitations is  the representations of unseen intents , which cannot be learned in the training stage. 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. Under this framework , 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 based on the learned intent representations. Since the purpose of DIR is to provide better intent representations, it can be easily integrated with existing ZSID and GZSID methods. Experiments on two real-world datasets show that the proposed framework brings consistent improvement to the baseline systems , regardless of the model architectures or zero-shot learning strategies .
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 .
D
, when the label names are given in the form of raw phrases or sentences
R
factor
problem
A
that
D
, which
R
class-transductive
novel
R
Disentangled
Class-Transductive
R
DIR
CTIR
R
in the trainingstage
during training
R
Under this framework
On this basis
R
based on the learned intent representations. Since the purpose of DIR is to provide better intent representations, it can be easily
. CTIR is easy to implement and can be
D
ZSID and GZSID
R
the proposed framework brings consistent
CTIR brings considerable
D
, regardless of the model architectures or zero-shot learning strategies