Deep Learning on Graphs for Natural Language Processing

Abstract

Recently, there has been a surge of interest in applying deep learning on graphs techniques (i.e., Graph Neural Networks (GNNs)) to NLP, and has achieved considerable success in many NLP tasks. Despite these successes, deep learning on graphs for NLP still face many challenges, including automatically transforming textual data into highly graph-structured data, and effectively modeling complex data that involves mapping between graph-based inputs and other highly structured output data such as sequences, trees, and graph data with multi-types in both nodes and edges. This tutorial will cover relevant and interesting topics on applying deep learning on graphs techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, GNN-based encoder-decoder models for NLP, and the applications of GNNs in various NLP tasks (e.g., information extraction, machine translation and question answering). In addition, a hands-on demo session will be included to help the audience gain practical experience on applying GNNs to solve challenging NLP problems using our recently developed open source library – Graph4NLP, the first library for researchers and practitioners for easy use of GNNs for various NLP tasks.