Deep Unknown Intent Detection with Margin Loss
This addresses a challenging task in dialogue systems for improving intent recognition, but it appears incremental as it builds on existing methods.
The paper tackles the problem of detecting unknown user intents in dialogue systems by proposing a two-stage method using BiLSTM with margin loss for feature extraction and LOF for novelty detection, achieving consistent improvements on benchmark datasets.
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.