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

Stochastic Natural Language Generation Using Dependency Information