  Spoken language understanding (SLU) systems are widely used in handling of
customer-care calls.A traditional SLU system consists of an acoustic model (AM)
and a language model (LM) that areused to decode the utterance and a natural
language understanding (NLU) model that predicts theintent. While AM can be
shared across different domains, LM and NLU models need to be
trainedspecifically for every new task. However, preparing enough data to train
these models is prohibitivelyexpensive. In this paper, we introduce an
efficient method to expand the limited in-domain data. Theprocess starts with
training a preliminary NLU model based on logistic regression on the
in-domaindata. Since the features are based onn= 1,2-grams, we can detect the
most informative n-gramsfor each intent class. Using these n-grams, we find the
samples in the out-of-domain corpus that1) contain the desired n-gram and/or 2)
have similar intent label. The ones which meet the firstconstraint are used to
train a new LM model and the ones that meet both constraints are used to train
anew NLU model. Our results on two divergent experimental setups show that the
proposed approachreduces by 30% the absolute classification error rate (CER)
comparing to the preliminary modelsand it significantly outperforms the
traditional data expansion algorithms such as the ones based onsemi-supervised
learning, TF-IDF and embedding vectors.
