Deep Open Intent Classification with Adaptive Decision Boundary

Authors

  • Hanlei Zhang State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China, Beijing National Research Center for Information Science and Technology (BNRist), Beijing 100084, China
  • Hua Xu State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China, Beijing National Research Center for Information Science and Technology (BNRist), Beijing 100084, China
  • Ting-En Lin State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China, Beijing National Research Center for Information Science and Technology (BNRist), Beijing 100084, China

Keywords:

Conversational AI/Dialog Systems, (Deep) Neural Network Algorithms, Classification and Regression, Anomaly/Outlier Detection

Abstract

Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance the performances of both known intents and the open intent. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we automatically learn the adaptive spherical decision boundary for each known class with the aid of well-trained features. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open intent samples and is free from modifying the model architecture. Moreover, our approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods.

Downloads

Published

2021-05-18

How to Cite

Zhang, H., Xu, H., & Lin, T.-E. (2021). Deep Open Intent Classification with Adaptive Decision Boundary. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14374-14382. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17690

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing III