Causal events play a crucial role in explaining the intricate relationships between the causes and effects of events. However, comprehending causal events within discourse, text, or speech poses significant semantic challenges. We propose a contrastive learning-based method in this submission to the Causal News Corpus - Event Causality Shared Task 2023, with a specific focus on SubTask1 centered on causal event classification. In our approach we pre-train our base model using Supervised Contrastive (SuperCon) learning. Subsequently, we fine-tune the pre-trained model for the specific task of causal event classification. Our experimentation demonstrates the effectiveness of our method, achieving a competitive performance, and securing the 2nd position on the leaderboard with an F1-Score of 84.36.

We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations. Our work also shows that jointly modeling both structured knowledge tuples and language improves both.