@article{M4BC5D534,
title = "Sentence Generation from Knowledge Base Triples Using Attention Mechanism Encoder-decoder",
journal = "Journal of KIISE, JOK",
year = "2019",
issn = "2383-630X",
doi = "10.5626/JOK.2019.46.9.934",
author = "Garam Choi,Sung-Pil Choi",
keywords = "sentence generation,natural language generation,sentence generation model,generative model",
abstract = "In this paper, we have investigated the generation of natural language sentences by using Knowledge Base Triples data with a structured structure. In order to generate a sentence that expresses the triple, a LSTM (Long Short-term Memory Network) encoder-decoder structure is used along with an Attention Mechanism. The BLEU score and ROUGE score for the test data were 42.264 (BLEU-1), 32.441 (BLEU-2), 26.820 (BLEU-3), 24.446 (BLEU-4), and 47.341 and 0.8% (based on BLEU-1) for the data comparison model. In addition, the average of the top 10 test data BLEU scores was recorded as 99.393 (BLEU-1)."
}