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• W2998987095 abstract "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." @default.
• W2998987095 created "2020-01-23" @default.
• W2998987095 creator A5024773257 @default.
• W2998987095 creator A5035899637 @default.
• W2998987095 date "2020-01-12" @default.
• W2998987095 modified "2023-09-27" @default.
• W2998987095 title "Stochastic Natural Language Generation Using Dependency Information." @default.
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• W2998987095 hasPublicationYear "2020" @default.
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