Event Causality Identification (ECI) is one of the main tasks in Natural Language Processing (NLP) especially in extracting causal relationships from text. Since identifying causality requires significant time and resources, we applied various data augmentation techniques to enhance data efficiency and the model's classification performance. In this context, preserving causality by maintaining the sentence's structure and context is crucial. Therefore, we propose an augmentation method that leverages the characteristics of Pre-trained Language Models (PLMs) that learn context during the masking process. To compare with PLM-based approaches, we employed various augmentation techniques such as Easy Data Augmentation (EDA), part-of-speech (pos) tagging, noise-based methods and Large Language Models (LLMs). We evaluate performance using Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA) as the downstream binary classifier whether the sentence involves the causal relationship. To examine the impact of selection strategy, we compared two approaches: cosine similarity-based Top-1 selection and random selection among augmented candidates. The selected sentences were then used to train the ELECTRA classifier. In addition to standard evaluation, we design an imbalanced data scenario to assess the robustness of the proposed method under low-resource conditions. PLMs showed the highest performance and demonstrated their applicability across various environments by maintaining strong performance even in imbalanced scenarios. Through our experiments, we confirmed that PLM-based data augmentation methods achieve meaningful results in the ECI task and demonstrate the importance of preserving context and structure for predicting causality in sentences.
Chun et al. (Mon,) studied this question.