  Self-attention networks (SAN) have shown promising performance in various
Natural Language Processing (NLP) scenarios, especially in machine translation.
One of the main points of SANs is the strength of capturing long-range and
multi-scale dependencies from the data. In this paper, we present a novel
intent detection system which is based on a self-attention network and a
Bi-LSTM. Our approach shows improvement by using a transformer model and deep
averaging network-based universal sentence encoder compared to previous
solutions. We evaluate the system on Snips, Smart Speaker, Smart Lights, and
ATIS datasets by different evaluation metrics. The performance of the proposed
model is compared with LSTM with the same datasets.
