A Three-Stage Pipeline for Conditional Entity and Relation Extraction

Traditional entity and relation extraction (ERE) task aims at extracting (subject, object, predicate) triplets from natural language texts. However, this ignores the prerequisites for some knowledge. CHIP2022 TASK2 first uses condition spans to support causal triplets. We expand it to the conditional entity and relation extraction task (Cond-ERE), the goal of which is to extract condition spans for conditional triplets on the basis of traditional ERE tasks. Currently, the one-step joint extraction model is almost unable to extract condition spans. Therefore, based on the existing models, we present a three-stage pipeline: first use one model for ERE to jointly extract triplets with all unconditional relations, then for each conditional one use a separate model to extract triplets, and finally use the Machine Reading Comprehension (MRC) model to judge whether these triplets with conditional relations are actually conditional or not and extract condition spans at the same time. Experiment results show that our three-stage pipeline can better extract triplets for multiple relations, and has significant advantages to extract condition spans. Besides, The MRC query template we designed further improves the performance of condition span extraction.