  The goal of this paper is to learn cross-domain representations for slot
filling task in spoken language understanding (SLU). Most of the recently
published SLU models are domain-specific ones that work on individual task
domains. Annotating data for each individual task domain is both financially
costly and non-scalable. In this work, we propose an adversarial training
method in learning common features and representations that can be shared
across multiple domains. Model that produces such shared representations can be
combined with models trained on individual domain SLU data to reduce the amount
of training samples required for developing a new domain. In our experiments
using data sets from multiple domains, we show that adversarial training helps
in learning better domain-general SLU models, leading to improved slot filling
F1 scores. We further show that applying adversarial learning on domain-general
model also helps in achieving higher slot filling performance when the model is
jointly optimized with domain-specific models.
