LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification
This work addresses the data scarcity issue for researchers and practitioners in natural language processing, specifically in event causality identification, by providing an incremental improvement over existing methods.
The paper tackles the data lacking problem in event causality identification by introducing LearnDA, a learnable knowledge-guided data augmentation method that generates new training examples using a dual learning framework, resulting in F1 score improvements of +2.5 and +2.1 points on EventStoryLine and Causal-TimeBank benchmarks, respectively.
Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLP-related augmentation methods cannot directly produce the available data required for this task. To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. On the one hand, our approach is knowledge-guided, which can leverage existing knowledge bases to generate well-formed new sentences. On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework and can interactively adjust the generation process to generate task-related sentences. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related training data for ECI; 2) our method outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.5 and +2.1 points on F1 value respectively).