Abstract
As a data augmentation method, masking word is commonly used in many natural language processing tasks. However, most mask methods are based on rules and are not related to downstream tasks. In this paper, we propose a novel masking word generator, named Actor-Critic Mask Model (ACMM), which can adaptively adjust the mask strategy according to the performance of downstream tasks. In order to demonstrate the effectiveness of the method, we conducted experiments on two causal event extraction datasets. Experiment results show that, compared with various rule-based masking methods, the masked sentences generated by our proposed method can significantly enhance the generalization of the model and improve the model performance.
Highlights
Constructing a causal knowledge graph has essential impact on natural language reasoning tasks
Causal event extraction was formulated as a pipeline process [1]
The first part of the table is the traditional model that does not use the BERT pre-trained model, the second part is the model that uses BERT as encoder, and the third part is the second part of the model with our adversarial model components
Summary
Constructing a causal knowledge graph has essential impact on natural language reasoning tasks. How to extract causal event pairs from texts is a fundamental problem. Causal event extraction was formulated as a pipeline process [1]. It identifies all potential event pairs and performs causal relation classification for each of them. This extraction method tends to suffer from the error propagation problem, which means the error from the extracted event pair will be passed to the classification process. One way to mitigate this effect is a sort of sequence labeling task [2]
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