  Out-of-Domain (OOD) intent detection is important for practical dialog
systems. To alleviate the issue of lacking OOD training samples, some works
propose synthesizing pseudo OOD samples and directly assigning one-hot OOD
labels to these pseudo samples. However, these one-hot labels introduce noises
to the training process because some hard pseudo OOD samples may coincide with
In-Domain (IND) intents. In this paper, we propose an adaptive soft pseudo
labeling (ASoul) method that can estimate soft labels for pseudo OOD samples
when training OOD detectors. Semantic connections between pseudo OOD samples
and IND intents are captured using an embedding graph. A co-training framework
is further introduced to produce resulting soft labels following the smoothness
assumption, i.e., close samples are likely to have similar labels. Extensive
experiments on three benchmark datasets show that ASoul consistently improves
the OOD detection performance and outperforms various competitive baselines.
