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

一、Background

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

拼接前后向最有一个时间步的输出。

2. Large Margin Cosine Loss (LMCL)

Transforms softmax loss into cosine loss by applying L2 normalization on both features and weight vectors.

ICLR 2019)DO DEEP GENERATIVE MODELS KNOW WHAT THEY DON’T KNOW?

3. Local Outlier Factor (LOF)

利用k-近邻找异常点。

可参考基于k-近邻的多元时间序列局部异常检测

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

五、Refs

paper/基于k-近邻的多元时间序列局部异常检测