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Statistical methods for dialogue systems

Georgiadou Despoina

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URI: http://purl.tuc.gr/dl/dias/4190BB31-6107-4D32-8158-3471C4E3E01A
Year 2017
Type of Item Master Thesis
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Summary

A prevalent and challenging task in spoken language understanding is slot filling. Currently, the best approaches in this domain are based on recurrent neural networks (RNNs). However, in their simplest form, RNNs cannot learn long-term dependencies in the data. In this work, we propose the use of ClockWork recurrent neural network (CW-RNN) architectures in the slot-filling domain. CW-RNN is a multi-timescale implementation of the simple RNN rchitecture, which has proven to be powerful since it maintains relativelysmall model complexity. In addition, CW-RNN exhibits a great ability to model long-term memory inherently. In our experiments on the ATIS benchmark data set, we also evaluate several novel variants of CW-RNN and we find that they significantly outperform simple RNNs and they achieve results among the state-of- the-art, while retaining smaller complexity.

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