Scene Generation from a Sentence by Learning Object Relation

Yongmin Shin, Su Jeong Choi, Seong-Bae Park, Seyoung Park

http://doi.org/10.5626/JOK.2019.46.5.431

In communication between humans and machines, location information is crucial. However, it is sometimes omitted. While humans can infer omitted information, machines cannot. Thus, certain problems can occur when generating scenes from sentences. In order to solve this problem, previous studies have found an explicit relation in the sentence, then inferred an implicit relation by using prior probability. However, such methods are not suitable for Korean, as it has morphologically productivity. In this paper, we suggest a scene-generation method for Korean. Frist, we find an explicit relation by using an RNN-based artificial neural network. Then, to infer implicit information, we use the prior probability of relations. Finally, we prepare a scene tree with the obtained information, then generate a scene using that tree. In order to evaluate the scene generation, we measure the accuracy of the model dealing with the relationship and assign a human score to the generated scene. As a result, the method is proven to be effective with excellent performance and evaluation.