2015 UsingRecurrentNeuralNetworksfor

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2016

  • (Liu & Lane, 2016) ⇒ Bing Liu, and Ian Lane. (2016). “Attention-based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling.” In: Proceedings of Interspeech-2016.

Quotes

Abstract

Semantic slot filling is one of the most challenging problems in spoken language understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for this task, and present several novel architectures designed to efficiently model past and future temporal dependencies. Specifically, we implemented and compared several important RNN architectures, including Elman, Jordan, and hybrid variants. To facilitate reproducibility, we implemented these networks with the publicly available Theano neural network toolkit and completed experiments on the well-known airline travel information system (ATIS) benchmark. In addition, we compared the approaches on two custom SLU data sets from the entertainment and movies domains. Our results show that the RNN-based models outperform the conditional random field (CRF) baseline by 2% in absolute error reduction on the ATIS benchmark. We improve the state-of-the-art by 0.5% in the Entertainment domain, and 6.7% for the movies domain.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 UsingRecurrentNeuralNetworksforYoshua Bengio
Dong Yu
Li Deng
Geoffrey Zweig
Xiaodong He
Larry Heck
Yann N. Dauphin
Grégoire Mesnil
Kaisheng Yao
Dilek Hakkani-Tur
Gokhan Tur		Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding10.1109/TASLP.2014.23836142015
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