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Semantically Conditioned LSTM based Natural Language Generation for Spoken Dialogue Systems

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Figure 1: Semantic Controlled LSTM cell pro-posed in this paper. The upper part is a traditionalLSTM cell in charge of surface realisation, whilethe lower part is a sentence planning cell based ona sigmoid control gate and a dialogue act (DA).
Figure 2: The Deep LSTM generator structure by stacking multiple LSTM layers on top of the DA cell.The skip connection was adopted to mitigate the vanishing gradient, while the dropout was applied ondashed connections to prevent co-adaptation and overfitting.
Table 1: Ontologies used in the experiments.
Table 4: Pairwise preference test among four sys-tems. Statistical significance was computed usingtwo-tailed binomial test.
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