  Recently advancements in sequence-to-sequence neural network architectures
have led to an improved natural language understanding. When building a neural
network-based Natural Language Understanding component, one main challenge is
to collect enough training data. The generation of a synthetic dataset is an
inexpensive and quick way to collect data. Since this data often has less
variety than real natural language, neural networks often have problems to
generalize to unseen utterances during testing. In this work, we address this
challenge by using multi-task learning. We train out-of-domain real data
alongside in-domain synthetic data to improve natural language understanding.
We evaluate this approach in the domain of airline travel information with two
synthetic datasets. As out-of-domain real data, we test two datasets based on
the subtitles of movies and series. By using an attention-based encoder-decoder
model, we were able to improve the F1-score over strong baselines from 80.76 %
to 84.98 % in the smaller synthetic dataset.
