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New Recurrent Neural Network Variants for Sequence Labeling

Abstract : In this paper we study different architectures of Recurrent Neural Networks (RNN) for sequence labeling tasks. We propose two new variants of RNN and we compare them to the more traditional RNN architectures of Elman and Jordan. We explain in details the advantages of these new variants of RNNs with respect to Elman's and Jordan's RNN. We evaluate all models, either new or traditional, on three different tasks: POS-tagging of the French Treebank, and two tasks of Spoken Language Understanding (SLU), namely ATIS and MEDIA. The results we obtain clearly show that the new variants of RNN are more effective than the traditional ones.
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Contributor : Marco Dinarelli <>
Submitted on : Tuesday, March 14, 2017 - 5:02:46 PM
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  • HAL Id : hal-01489955, version 1



Marco Dinarelli, Isabelle Tellier. New Recurrent Neural Network Variants for Sequence Labeling. 17th International Conference on Intelligent Text Processing and Computational Linguistics, Apr 2016, Konya, Turkey. ⟨hal-01489955⟩



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