Reliable pseudo-labeling prediction framework for new event type induction

As a subtask of open domain event extraction(ODEE),new event type induction aims to discover a set of unseen event types from a given corpus.Existing methods mostly adopt semi-supervised or unsupervised learning to achieve the goal,which uses complex and different objective functions for labeled and unlabeled data respectively.In order to unify and simplify objective functions,a reliable pseudo-labeling prediction(RPP)framework for new event type induction was proposed.The framework introduces a double label reassignment(DLR)strategy for unlabeled data based on swap-prediction.DLR strategy can alleviate the model degeneration caused by swap-predication and further combine the real distribution over unseen event types to produce more reliable pseudo labels for unlabeled data.The generated reliable pseudo labels help the overall model be optimized by a unified and simple objective.Experiments show that RPP framework outperforms the state-of-the-art on the benchmark.