Document-level event argument extraction with self-augmentation and a cross-domain joint training mechanism

Highlights：

• A new approach for resolving the data sparsity issue in document-level event argument extraction from two complementing viewpoints.

• A self-augmentation technique that combines pre-trained language models and a label-conditioned pre-training procedure to retain word-label consistency.

• A cross-domain joint training framework for knowledge transfer from datasets of varied granularity, task specifications, and the event schema description language.

• A novel noise filtering method in teacher–student framework to mitigate the data quality issue.

• State-of-the-art performance on standard datasets.