Understanding spoken language is a highly complex problem, which can be
decomposed into several simpler tasks. In this paper, we focus on Spoken
Language Understanding (SLU), the module of spoken dialog systems responsible
for extracting a semantic interpretation from the user utterance. The task is
treated as a labeling problem. In the past, SLU has been performed with a wide
variety of probabilistic models. The rise of neural networks, in the last
couple of years, has opened new interesting research directions in this domain.
Recurrent Neural Networks (RNNs) in particular are able not only to represent
several pieces of information as embeddings but also, thanks to their recurrent
architecture, to encode as embeddings relatively long contexts. Such long
contexts are in general out of reach for models previously used for SLU. In
this paper we propose novel RNNs architectures for SLU which outperform
previous ones. Starting from a published idea as base block, we design new deep
RNNs achieving state-of-the-art results on two widely used corpora for SLU:
ATIS (Air Traveling Information System), in English, and MEDIA (Hotel
information and reservation in France), in French.Comment: 8 pages. Rejected from IJCAI 2017, good remarks overall, but slightly
off-topic as from global meta-reviews. Recommendations: 8, 6, 6, 4. arXiv
admin note: text overlap with arXiv:1706.0174