There are a rich variety of NLP problems that can be best expressed with graph structures. Due to the great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graphs techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems, and have already achieved great success. As a result, there is a new wave of research at the intersection of deep learning on graphs and NLP which has influenced a variety of NLP tasks, ranging from classification tasks like sentence classification, semantic role labeling, and relation extraction, to generation tasks like machine translation, question generation, and summarization. Despite these successes, deep learning on graphs for NLP still faces many challenges, including but not limited to 1) automatically transforming original text into highly graph-structured data, 2) graph representation learning for complex graphs (e.g., multi-relational graphs, heterogeneous graphs), 3) learning the mapping between complex data structures (e.g., Graph2Seq, Graph2Tree, Graph2Graph).

This workshop aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to the above challenges. This workshop intends to share visions of investigating new approaches and methods at the intersection of graph machine learning and NLP. The workshop will consist of contributed talks, contributed posters, invited talks, and panelists on a wide variety of novel GNN methods and NLP applications.