We invite submission of papers describing innovative research and applications around the following topics. Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain is encouraged.

1. Automatic graph construction for NLP
2. Graph representation learning for NLP
3. Graph2seq, graph2tree, and graph2graph models for NLP
4. Deep reinforcement learning on graphs for NLP
5. Adversarial deep learning on graphs for NLP
6. GNN based representation learning on knowledge graphs

Paper submission (GMT)
We welcome papers ranging between 4 - 8 pages, excluding all references and appendices. All submissions should be formatted in the ICLR 2022 format. A LaTeX template is available here. Following the ICLR conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well-executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible. The accepted papers will be posted on the workshop website and will not appear in the ICLR proceedings.