A spoken dialogue system, comprising: a first processing circuitry; a recurrent neural network operably connected to the first processing circuitry; and one or more storage devices (140, 810, 930) storing computer executable instructions that when executed by the first processing circuitry and the recurrent neural network operably connected to the first processing circuitry, cause the first processing circuitry and the recurrent neural network to perform a method comprising: processing, by the first processing circuitry, one or more dialogue act slot-value representations to produce a first representation of the one or more dialogue act slot-value representations, wherein each dialogue act slot-value representation comprises a first vector representing a one-hot vector of a delexicalized dialogue act and a delexicalized slot and a second vector comprising a mean of one or more word embeddings for all of the words corresponding to a lexicalized value associated with the delexicalized slot of the first vector; and processing, by the recurrent neural network operably connected to the first processing circuitry, the first representation and a second representation to produce one or more delexicalized sentences as an output, the second representation representing all of the delexicalized slots expected to be included in the natural language output; wherein the first processing circuitry comprises a bi-directional long short-term memory, LSTM, recurrent neural network connected to a computing circuit; and wherein the recurrent neural network operably connected to the first processing circuitry is a semantically controlled long short-term memory, sc-LSTM, recurrent neural network. a first processing circuitry; a recurrent neural network operably connected to the first processing circuitry; and one or more storage devices (140, 810, 930) storing computer executable instructions that when executed by the first processing circuitry and the recurrent neural network operably connected to the first processing circuitry, cause the first processing circuitry and the recurrent neural network to perform a method comprising: processing, by the first processing circuitry, one or more dialogue act slot-value representations to produce a first representation of the one or more dialogue act slot-value representations, wherein each dialogue act slot-value representation comprises a first vector representing a one-hot vector of a delexicalized dialogue act and a delexicalized slot and a second vector comprising a mean of one or more word embeddings for all of the words corresponding to a lexicalized value associated with the delexicalized slot of the first vector; and processing, by the recurrent neural network operably connected to the first processing circuitry, the first representation and a second representation to produce one or more delexicalized sentences as an output, the second representation representing all of the delexicalized slots expected to be included in the natural language output; wherein the first processing circuitry comprises a bi-directional long short-term memory, LSTM, recurrent neural network connected to a computing circuit; and wherein the recurrent neural network operably connected to the first processing circuitry is a semantically controlled long short-term memory, sc-LSTM, recurrent neural network. processing, by the first processing circuitry, one or more dialogue act slot-value representations to produce a first representation of the one or more dialogue act slot-value representations, wherein each dialogue act slot-value representation comprises a first vector representing a one-hot vector of a delexicalized dialogue act and a delexicalized slot and a second vector comprising a mean of one or more word embeddings for all of the words corresponding to a lexicalized value associated with the delexicalized slot of the first vector; and processing, by the recurrent neural network operably connected to the first processing circuitry, the first representation and a second representation to produce one or more delexicalized sentences as an output, the second representation representing all of the delexicalized slots expected to be included in the natural language output; wherein the first processing circuitry comprises a bi-directional long short-term memory, LSTM, recurrent neural network connected to a computing circuit; and wherein the recurrent neural network operably connected to the first processing circuitry is a semantically controlled long short-term memory, sc-LSTM, recurrent neural network.