Papers › ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations

ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations

25 Mar 2022NAACL (ACL) 2022 7arXiv:2203.13602archive 2025-07-28

Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle, Eneko Agirre, Bonan Min

The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples. In this demonstration we introduce a new workflow where the analyst directly verbalizes the entities/relations, which are then used by a Textual Entailment model to perform zero-shot IE. We present the design and implementation of a toolkit with a user interface, as well as experiments on four IE tasks that show that the system achieves very good performance at zero-shot learning using only 5--15 minutes per type of a user's effort. Our demonstration system is open-sourced at https://github.com/BBN-E/ZS4IE . A demonstration video is available at https://vimeo.com/676138340 .

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