  This article presents a stochastic corpus-based model for generating natural
language text. Our model first encodes dependency relations from training data
through a feature set, then concatenates these features to produce a new
dependency tree for a given meaning representation, and finally generates a
natural language utterance from the produced dependency tree. We test our model
on nine domains from tabular, dialogue act and RDF format. Our model
outperforms the corpus-based state-of-the-art methods trained on tabular
datasets and also achieves comparable results with neural network-based
approaches trained on dialogue act, E2E and WebNLG datasets for BLEU and ERR
evaluation metrics. Also, by reporting Human Evaluation results, we show that
our model produces high-quality utterances in aspects of informativeness and
naturalness as well as quality.
