  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).
