- Kim and Kim (INTERSPEECH2018)Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates still need out-of-domain samples。
- Yu et al., 2017(IJCAI2017)Open Category Classification by Adversarial Sample Generation
tries to generate positive and negative examples from known classes by using adversarial learning to augment training data, but does not work well in the discrete data space like text.
- Brychcin and Kra´l(EACL2017)Unsupervised Dialogue Act Induction using Gaussian Mixtures tries to model intents through clustering. Still, it does not make good use of prior knowledge provided by known intents and clustering results are usually unsatisfactory.
- LMCL:Wang et al. (CVPR 2018b)Cosface: Large margin cosine loss for deep face recognition
- We propose a two-stage method for unknown intent detection with BiLSTM;
- We introduce margin loss on BiLSTM to learn discriminative deep features, which is suitable for the detection task;
- Experiments conducted on two benchmark dialogue datasets show the effectiveness of the proposed method.
2. Large Margin Cosine Loss (LMCL)
Transforms softmax loss into cosine loss by applying L2 normalization on both features and weight vectors.
3. Local Outlier Factor (LOF)
- Maximum Softmax Probability (MSP)
- DOC：SOTA method in the field of open-world classification（EMNLP2017）