Complex natural language understanding modules in dialog systems have a
richer understanding of user utterances, and thus are critical in providing a
better user experience. However, these models are often created from scratch,
for specific clients and use cases, and require the annotation of large
datasets. This encourages the sharing of annotated data across multiple
clients. To facilitate this we introduce the idea of intent features: domain
and topic agnostic properties of intents that can be learned from the syntactic
cues only, and hence can be shared. We introduce a new neural network
architecture, the Global-Local model, that shows significant improvement over
strong baselines for identifying these features in a deployed, multi-intent
natural language understanding module, and, more generally, in a classification
setting where a part of an utterance has to be classified utilizing the whole
context.

提出从句法线索中学习机器人用户意图特征的思想，旨在提高自然语言理解模型的性能，特别是多意图的分类任务。提出了新的神经网络 Global-Local 模型，可在实际情况下和已有基线相比显着提高性能。