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A Deep Ensemble Model with Slot Alignment for Sequence to Sequence Natural Language Generation

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Table 2: Overview of the number of samples, as wellas different DA and slot types, in each dataset .
Table 3: Average number of sentences in the referenceutterance for a given number of slots in the correspond-ing MR, along with the proportion of MRs with specificslot counts.
Figure 3:Standard architecture of a single-layerencoder-decoder LSTM model with attention
Table 5: Contrastive example of a simple and a moreelegant reference utterance style for the same MR inthe E2E dataset.
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