  Few-shot slot tagging is an emerging research topic in the field of Natural
Language Understanding (NLU). With sufficient annotated data from source
domains, the key challenge is how to train and adapt the model to another
target domain which only has few labels. Conventional few-shot approaches use
all the data from the source domains without considering inter-domain relations
and implicitly assume each sample in the domain contributes equally. However,
our experiments show that the data distribution bias among different domains
will significantly affect the adaption performance. Moreover, transferring
knowledge from dissimilar domains will even introduce some extra noises so that
affect the performance of models. To tackle this problem, we propose an
effective similarity-based method to select data from the source domains. In
addition, we propose a Shared-Private Network (SP-Net) for the few-shot slot
tagging task. The words from the same class would have some shared features. We
extract those shared features from the limited annotated data on the target
domain and merge them together as the label embedding to help us predict other
unlabelled data on the target domain. The experiment shows that our method
outperforms the state-of-the-art approaches with fewer source data. The result
also proves that some training data from dissimilar sources are redundant and
even negative for the adaption.
