Learning Disentangled Intent Representations for Zero-shot Intent Detection

• URL: http://arxiv.org/abs/2012.01721v1
• Date: Thu, 3 Dec 2020 06:41:09 GMT
• Title: Learning Disentangled Intent Representations for Zero-shot Intent Detection
• Authors: Qingyi Si, Yuanxin Liu, Peng Fu, Jiangnan Li, Zheng Lin and Weiping Wang
• Abstract summary: We propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Representations (DIR) Under this framework, we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents. Experiments on two real-world datasets show that the proposed framework brings consistent improvement to the baseline systems.
• Score: 13.19024497857648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 training stage, 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.

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