  Scene graph is structured semantic representation that can be modeled as a
form of graph from images and texts. Image-based scene graph generation
research has been actively conducted until recently, whereas text-based scene
graph generation research has not. In this paper, we focus on the problem of
scene graph parsing from textual description of a visual scene. The core idea
is to use abstract meaning representation (AMR) instead of the dependency
parsing mainly used in previous studies. AMR is a graph-based semantic
formalism of natural language which abstracts concepts of words in a sentence
contrary to the dependency parsing which considers dependency relationships on
all words in a sentence. To this end, we design a simple yet effective
two-stage scene graph parsing framework utilizing abstract meaning
representation, SGRAM (Scene GRaph parsing via Abstract Meaning
representation): 1) transforming a textual description of an image into an AMR
graph (Text-to-AMR) and 2) encoding the AMR graph into a Transformer-based
language model to generate a scene graph (AMR-to-SG). Experimental results show
the scene graphs generated by our framework outperforms the dependency
parsing-based model by 11.61\% and the previous state-of-the-art model using a
pre-trained Transformer language model by 3.78\%. Furthermore, we apply SGRAM
to image retrieval task which is one of downstream tasks for scene graph, and
confirm the effectiveness of scene graphs generated by our framework.
