Deep Unknown Intent Detection with Margin Loss
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.
Code (1)
Tasks
Intent DetectionNovelty DetectionOpen Intent DetectionSimilar Papers 제목 키워드 기반
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning
Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of OOD detection is to learn discriminative semantic features. Traditional cross-entropy l…
Contrastive LearningOut of Distribution (OOD) DetectionUnknown Intent Detection Using Multi-Objective Optimization on Deep Learning Classifiers
Modelling and understanding dialogues in a conversation depends on identifying the user intent from the given text. Unknown or new intent detection is a critical task, as in a realistic scenario a user intent may frequen…
Intent DetectionIntent DiscoveryUnknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification
User intent classification plays a vital role in dialogue systems. Since user intent may frequently change over time in many realistic scenarios, unknown (new) intent detection has become an essential problem, where the …
ClassificationGeneral Classificationintent-classificationIntent Classification+3Towards Multi-label Unknown Intent Detection
Multi-class unknown intent detection has made remarkable progress recently. However, it has a strong assumption that each utterance has only one intent, which does not conform to reality because utterances often have mul…
Intent DetectionLong-tail Detection with Effective Class-Margins
Large-scale object detection and instance segmentation face a severe data imbalance. The finer-grained object classes become, the less frequent they appear in our datasets. However, at test-time, we expect a detector tha…
Binary ClassificationInstance SegmentationLong-tailed Object DetectionObject+3