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The recent surge of intelligent personal assistants motivates spoken language understanding of dialogue systems. However, the domain constraint along with the inflexible intent schema remains a big issue. This paper focuses on the task of intent expansion, which helps remove the domain limit and make an intent schema flexible. A convolutional deep structured semantic model (CDSSM) is applied to jointly learn the representations for human intents and associated utterances.

Many state-of-the-art solutions for the understanding of speech data
have in common to be probabilistic and to rely on machine learning
algorithms to train their models from large amount of data. The
difficulty remains in the cost of collecting and annotating such data.
Another point is the time for updating an existing model to a new domain.
Recent works showed that a zero-shot learning method allows
to bootstrap a model with good initial performance. To do so, this
method relies on exploiting both a small-sized ontological description