Summary

In this chapter, we covered a wide range of topics, starting with the fundamental concepts of graph representations in NLP and progressing through various applications. These applications include graph-based text summarization, IE using GNNs, OpenIE, mapping natural language to logic, question answering over knowledge graphs, and graph-based dialogue systems.

You learned that graph-based approaches offer powerful tools for enhancing various aspects of dialogue systems, from state tracking to response generation and policy learning. As research in this area continues to advance, we can expect to see even more sophisticated and capable dialogue systems that leverage the rich structural information provided by graph representations.

In the next chapter, we will go through some of the very common use cases of graph learning around recommendation systems.