  Spoken language understanding (SLU) systems can be trained on two types of
labelled data: aligned or unaligned. Unaligned data do not require word by word
annotation and is easier to be obtained. In the paper, we focus on spoken
language understanding from unaligned data whose annotation is a set of
act-slot-value triples. Previous works usually focus on improve slot-value pair
prediction and estimate dialogue act types separately, which ignores the
hierarchical structure of the act-slot-value triples. Here, we propose a novel
hierarchical decoding model which dynamically parses act, slot and value in a
structured way and employs pointer network to handle out-of-vocabulary (OOV)
values. Experiments on DSTC2 dataset, a benchmark unaligned dataset, show that
the proposed model not only outperforms previous state-of-the-art model, but
also can be generalized effectively and efficiently to unseen act-slot type
pairs and OOV values.
