【ACL-2019】Deep Unknown Intent Detection with Margin Loss

二、Contributions

• 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.

三、Method

1. BiLSTM

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)

四、Experiments

1. Baselines

• Maximum Softmax Probability (MSP)
• DOC：SOTA method in the field of open-world classification（EMNLP2017
• DOC(Softmax)

• LOF (Softmax)

2. Result