SPTEAE: A SOFT PROMPT TRANSFER MODEL FOR ZERO-SHOT CROSS-LINGUAL EVENT ARGUMENT EXTRACTION

In zero-shot cross-lingual event argument extraction(EAE) task, a model is typically trained on source language datasets and then applied on task language datasets. There is a trend to regard the zero-shot cross-lingual EAE task as a sequence generation task with manual prompts or discrete prompts. However, there are some problems with these prompts, including using suboptimal prompts and difficult to transfer from source language to target language. To overcome these issues, we propose a method called SPTEAE(A Soft Prompt Transfer model for zero-shot cross-lingual Event Argument Extraction). SPTEAE utilizes a sequence of tunable vectors which are tuned in source language as event type prompts. These source language event type prompts can be transferred as target prompts to perform target EAE task by key-value selection mechanism. For each event type, SPTEAE learns a special target prompt by attending to highly relevant source prompts. Experiment results show that the average performance of SPTEAE with soft prompt transfer is 2.6% higher than the current state-of-the-art model on the ACE2005 dataset.