Complex Question Enhanced Transfer Learning for Zero-shot Joint Information Extraction

Zero-shot information extraction (IE) tasks have attracted great attention recently. However, how to jointly model multiple IE tasks in the zero-shot scenario is still an open question. In this paper, we focus on zero-shot joint IE tasks and highlight how to transfer the knowledge of cross-task relations from the source domain to the target domain. To solve this problem, we first unify all IE tasks with a machine reading comprehension (MRC) framework, which can make the most of training data and enhance its ability on span extraction. Then, we generate complex questions to explicitly model cross-task relations with natural language descriptions, thereby providing prior knowledge for pre-defined types and building more general linkages among different entities and triggers as well. Specifically, we define three operations for generating templates for complex questions, i.e., intersecting , connecting , and composing . Besides, we design an efficient training strategy to exploit the synthetic data with complex questions. We evaluate our approach on four datasets from different domains for various IE tasks. Experimental results show the effectiveness of our approach in improving the performance of zero-shot joint IE tasks in multiple domains.