Sentence Generation from Knowledge Base Triples Using Attention Mechanism Encoder-decoder

Garam Choi, Sung-Pil Choi

http://doi.org/10.5626/JOK.2019.46.9.934

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).