NLP4ITF @ Causal News Corpus 2022: Leveraging Linguistic Information for Event Causality Classification

Abstract

We present our submission to Subtask 1 of the CASE-2022 Shared Task 3: Event Causality Identification with Causal News Corpus as part of the 5th Workshop on Challenges and Applications of Automated Extraction of Sociopolitical Events from Text (CASE 2022) (Tan et al., 2022a). The task focuses on causal event classification on the sentence level and involves differentiating between sentences that include a cause-effect relation and sentences that do not. We approached this as a binary text classification task and experimented with multiple training sets augmented with additional linguistic information. Our best model was generated by training roberta-base on a combination of data from both Subtasks 1 and 2 with the addition of named entity annotations. During the development phase we achieved a macro F1 of 0.8641 with this model on the development set provided by the task organizers. When testing the model on the final test data, we achieved a macro F1 of 0.8516.