  We propose a neural architecture with the main characteristics of the most
successful neural models of the last years: bidirectional RNNs,
encoder-decoder, and the Transformer model. Evaluation on three sequence
labelling tasks yields results that are close to the state-of-the-art for all
tasks and better than it for some of them, showing the pertinence of this
hybrid architecture for this kind of tasks.
