  In this paper, we propose a novel end-to-end sequence-to-sequence spoken
language understanding model using an attention mechanism. It reliably selects
contextual acoustic features in order to hypothesize semantic contents. An
initial architecture capable of extracting all pronounced words and concepts
from acoustic spans is designed and tested. With a shallow fusion language
model, this system reaches a 13.6 concept error rate (CER) and an 18.5 concept
value error rate (CVER) on the French MEDIA corpus, achieving an absolute 2.8
points reduction compared to the state-of-the-art. Then, an original model is
proposed for hypothesizing concepts and their values. This transduction reaches
a 15.4 CER and a 21.6 CVER without any new type of context.
