Deep Unknown Intent Detection with Margin Loss

06/02/2019
by   Ting-En Lin, et al.
0

Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance. Then, we feed the feature vectors to the density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that our method can yield consistent improvements compared with the baseline methods.

READ FULL TEXT
research
03/07/2020

A Post-processing Method for Detecting Unknown Intent of Dialogue System via Pre-trained Deep Neural Network Classifier

With the maturity and popularity of dialogue systems, detecting user's u...
research
05/29/2021

Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning

Detecting Out-of-Domain (OOD) or unknown intents from user queries is es...
research
04/16/2022

Learning to Classify Open Intent via Soft Labeling and Manifold Mixup

Open intent classification is a practical yet challenging task in dialog...
research
05/19/2021

Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?

Unsupervised outlier detection, which predicts if a test sample is an ou...
research
02/07/2020

Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions

Person re-identification (ReID) under occlusions is a challenging proble...
research
02/24/2021

Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition

In this paper, we propose a new deep neural network classifier that simu...
research
04/20/2023

Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary

Open intent classification, which aims to correctly classify the known i...

Please sign up or login with your details

Forgot password? Click here to reset