  Most End-to-End (E2E) SLU networks leverage the pre-trained ASR networks but
still lack the capability to understand the semantics of utterances, crucial
for the SLU task. To solve this, recently proposed studies use pre-trained NLU
networks. However, it is not trivial to fully utilize both pre-trained
networks; many solutions were proposed, such as Knowledge Distillation,
cross-modal shared embedding, and network integration with Interface. We
propose a simple and robust integration method for the E2E SLU network with
novel Interface, Continuous Token Interface (CTI), the junctional
representation of the ASR and NLU networks when both networks are pre-trained
with the same vocabulary. Because the only difference is the noise level, we
directly feed the ASR network's output to the NLU network. Thus, we can train
our SLU network in an E2E manner without additional modules, such as
Gumbel-Softmax. We evaluate our model using SLURP, a challenging SLU dataset
and achieve state-of-the-art scores on both intent classification and slot
filling tasks. We also verify the NLU network, pre-trained with Masked Language
Model, can utilize a noisy textual representation of CTI. Moreover, we show our
model can be trained with multi-task learning from heterogeneous data even
after integration with CTI.
