  End-to-end Spoken Language Understanding (SLU) models are made increasingly
large and complex to achieve the state-ofthe-art accuracy. However, the
increased complexity of a model can also introduce high risk of over-fitting,
which is a major challenge in SLU tasks due to the limitation of available
data. In this paper, we propose an attention-based SLU model together with
three encoder enhancement strategies to overcome data sparsity challenge. The
first strategy focuses on the transferlearning approach to improve feature
extraction capability of the encoder. It is implemented by pre-training the
encoder component with a quantity of Automatic Speech Recognition annotated
data relying on the standard Transformer architecture and then fine-tuning the
SLU model with a small amount of target labelled data. The second strategy
adopts multitask learning strategy, the SLU model integrates the speech
recognition model by sharing the same underlying encoder, such that improving
robustness and generalization ability. The third strategy, learning from
Component Fusion (CF) idea, involves a Bidirectional Encoder Representation
from Transformer (BERT) model and aims to boost the capability of the decoder
with an auxiliary network. It hence reduces the risk of over-fitting and
augments the ability of the underlying encoder, indirectly. Experiments on the
FluentAI dataset show that cross-language transfer learning and multi-task
strategies have been improved by up to 4:52% and 3:89% respectively, compared
to the baseline.
