Generating Text from Graph-based Data
Bachelor Thesis, Master Thesis
Recently, graph-to-sequence models have been applied to the task of text generation from structured data. Usually, these models incorporate Graph Neural Networks (GNNs) as graph encoders for learning effective graph representations. For instance, given a knowledge graph, we are interested in verbalizing a text that reproduces the information contained in the graph. The goal of this project is to develop and research deep learning techniques to generate textual information from graph-based data, such as meaning representations and knowledge graphs.