@article{M45380B79,
title = "Noise Injection for Natural Language Sentence Generation from Knowledge Base",
journal = "Journal of KIISE, JOK",
year = "2020",
issn = "2383-630X",
doi = "10.5626/JOK.2020.47.10.965",
author = "Sunggoo Kwon,Seyoung Park",
keywords = "natural language generation,sentence generation,noise injection,unsupervised learning,knowledge base",
abstract = "Generating a natural language sentence from Knowledge base is an operation of entering a triple in the Knowledge base to generate triple information, which is a natural language sentence containing the relationship between the entities. To solve the task of generating sentences from triples using a deep neural network, learning data consisting of many pairs of triples and natural language sentences are required. However, it is difficult to learn the model because the learning data composed in Korean is not yet released. To solve the deficiency of learning data, this paper proposes an unsupervised learning method that extracts keywords based on Korean Wikipedia sentence data and generates learning data using a noise injection technique. To evaluate the proposed method, we used gold-standard dataset produced by triples and sentence pairs. Consequently, the proposed noise injection method showed superior performances over normal unsupervised learning on various evaluation metrics including automatic and human evaluations."
}