@inproceedings{sainz-etal-2022-zs4ie,
title = "{ZS}4{IE}: A toolkit for Zero-Shot Information Extraction with simple Verbalizations",
author = "Sainz, Oscar and
Qiu, Haoling and
Lopez de Lacalle, Oier and
Agirre, Eneko and
Min, Bonan",
editor = "Hajishirzi, Hannaneh and
Ning, Qiang and
Sil, Avi",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-demo.4",
doi = "10.18653/v1/2022.naacl-demo.4",
pages = "27--38",
abstract = "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 \url{https://github.com/BBN-E/ZS4IE}. A demonstration video is available at \url{https://vimeo.com/676138340}.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations
%A Sainz, Oscar
%A Qiu, Haoling
%A Lopez de Lacalle, Oier
%A Agirre, Eneko
%A Min, Bonan
%Y Hajishirzi, Hannaneh
%Y Ning, Qiang
%Y Sil, Avi
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F sainz-etal-2022-zs4ie
%X 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.
%R 10.18653/v1/2022.naacl-demo.4
%U https://aclanthology.org/2022.naacl-demo.4
%U https://doi.org/10.18653/v1/2022.naacl-demo.4
%P 27-38
Markdown (Informal)
[ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations](https://aclanthology.org/2022.naacl-demo.4) (Sainz et al., NAACL 2022)
ACL
- Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle, Eneko Agirre, and Bonan Min. 2022. ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations, pages 27–38, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.