ZoIE: A Zero-Shot Open Information Extraction Model Based on Language Model

Open Information Extraction (Open IE) provides a method to extract triplets from text, which has become a research frontier and hot topic in recent years. However, the traditional open information extraction approaches have low precision and significant costs, which heavily rely on the artificially defined extraction paradigm. In this paper, we propose ZoIE, a zero-shot open information extraction model. First, we set positive and negative samples and use the pretraining method based on contrastive learning to train a language model. Then, we use the self-attention weight matrix to perform triplet extraction by using the method of nucleus sampling beam search. Finally, we construct a triplet fine-sorting strategy based on the loss function and select the optimal triplet from the candidate triplets. Experimental results on serveral datasets show that our proposed approach achieves better performance than other baselines.