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


Vol. 46,  No. 9, pp. 934-940, Sep.  2019
10.5626/JOK.2019.46.9.934


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  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).


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  Cite this article

[IEEE Style]

G. Choi and S. Choi, "Sentence Generation from Knowledge Base Triples Using Attention Mechanism Encoder-decoder," Journal of KIISE, JOK, vol. 46, no. 9, pp. 934-940, 2019. DOI: 10.5626/JOK.2019.46.9.934.


[ACM Style]

Garam Choi and Sung-Pil Choi. 2019. Sentence Generation from Knowledge Base Triples Using Attention Mechanism Encoder-decoder. Journal of KIISE, JOK, 46, 9, (2019), 934-940. DOI: 10.5626/JOK.2019.46.9.934.


[KCI Style]

최가람, 최성필, "주의집중 메커니즘을 통한 인코더-디코더 기반의 지식 베이스 트리플 활용 문장 생성," 한국정보과학회 논문지, 제46권, 제9호, 934~940쪽, 2019. DOI: 10.5626/JOK.2019.46.9.934.


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