Deep neural networks for identifying causal relations in texts - Chen, Siyuan (Nanyang Technological University, 2023)
Causal relation identification is an important task that facilitates many downstream tasks such as why-question answering, event prediction, information retrieval, sentiment analysis, etc. The goal of causal relation identification is to determine whether there's causal relation between 2 events or entities. With the prosperity of deep learning, end-to-end deep neural models have made great progress. Such approaches mainly focus on learning causal patterns from the training data, i.e. data-oriented. However, causal relation identification is a highly difficult task due to the flexible linguistic expressions and the background knowledge needed for identification. Despite of the necessity of learning from training data, only relying on it may suffer from poor generalizability. Hence, it's important to incorporate external knowledge into the neural network to improve its generalizability. In this thesis, we explore this idea in 2 subfields of causal relation identification, namely emotion-cause pair extraction and event causality identification.