计算机科学
训练集
领域(数学分析)
源代码
聚类分析
接头(建筑物)
编码(集合论)
任务(项目管理)
领域知识
人工智能
机器学习
培训(气象学)
情报检索
数据挖掘
数学分析
气象学
工程类
经济
建筑工程
集合(抽象数据类型)
管理
程序设计语言
物理
操作系统
数学
作者
W.B. An,Feng Tian,Ping Chen,Qinghua Zheng,Wei Ding
标识
DOI:10.1109/mis.2023.3283909
摘要
Discovering new user intents based on existing intents from constantly incoming unlabeled data is an important task in many intelligent systems deployed in the real world (e.g., dialogue systems). Since data with new intents are completely unlabeled, most current approaches employ clustering methods to generate pseudo labels to train their models. However, due to intent gaps between existing and new intents, pseudo labels generated by these models are noisy and prior knowledge from existing intents is not fully utilized. To mitigate these issues, we propose a robust Pseudo label Training and source domain Joint-training Network (PTJN) to refine the noisy pseudo labels and make full use of prior knowledge. Experimental results on three intent detection datasets show that our model is more effective and robust than state-of-the-art methods. The code and data are released at https://github.com/Lackel/PTJN .
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