  Out-of-scope intent detection is of practical importance in task-oriented
dialogue systems. Since the distribution of outlier utterances is arbitrary and
unknown in the training stage, existing methods commonly rely on strong
assumptions on data distribution such as mixture of Gaussians to make
inference, resulting in either complex multi-step training procedures or
hand-crafted rules such as confidence threshold selection for outlier
detection. In this paper, we propose a simple yet effective method to train an
out-of-scope intent classifier in a fully end-to-end manner by simulating the
test scenario in training, which requires no assumption on data distribution
and no additional post-processing or threshold setting. Specifically, we
construct a set of pseudo outliers in the training stage, by generating
synthetic outliers using inliner features via self-supervision and sampling
out-of-scope sentences from easily available open-domain datasets. The pseudo
outliers are used to train a discriminative classifier that can be directly
applied to and generalize well on the test task. We evaluate our method
extensively on four benchmark dialogue datasets and observe significant
improvements over state-of-the-art approaches. Our code has been released at
https://github.com/liam0949/DCLOOS.
