ISCA Archive Interspeech 2017
ISCA Archive Interspeech 2017

Label-Dependency Coding in Simple Recurrent Networks for Spoken Language Understanding

Marco Dinarelli, Vedran Vukotic, Christian Raymond

Modeling target label dependencies is important for sequence labeling tasks. This may become crucial in the case of Spoken Language Understanding (SLU) applications, especially for the slot-filling task where models have to deal often with a high number of target labels. Conditional Random Fields (CRF) were previously considered as the most efficient algorithm in these conditions. More recently, different architectures of Recurrent Neural Networks (RNNs) have been proposed for the SLU slot-filling task. Most of them, however, have been successfully evaluated on the simple ATIS database, on which it is difficult to draw significant conclusions. In this paper we propose new variants of RNNs able to learn efficiently and effectively label dependencies by integrating label embeddings. We show first that modeling label dependencies is useless on the (simple) ATIS database and unstructured models can produce state-of-the-art results on this benchmark. On ATIS our new variants achieve the same results as state-of-the-art models, while being much simpler. On the other hand, on the MEDIA benchmark, we show that the modification introduced in the proposed RNN outperforms traditional RNNs and CRF models.

doi: 10.21437/Interspeech.2017-1480

Cite as: Dinarelli, M., Vukotic, V., Raymond, C. (2017) Label-Dependency Coding in Simple Recurrent Networks for Spoken Language Understanding. Proc. Interspeech 2017, 2491-2495, doi: 10.21437/Interspeech.2017-1480

  author={Marco Dinarelli and Vedran Vukotic and Christian Raymond},
  title={{Label-Dependency Coding in Simple Recurrent Networks for Spoken Language Understanding}},
  booktitle={Proc. Interspeech 2017},