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    <abstract>We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize the task as a translation between task-specific input text and output triples. By taking the task-specific input, we enable a task-agnostic translation by leveraging the latent knowledge that a pre-trained language model has about the task. We further demonstrate that a simple pre-training task of predicting which relational information corresponds to which input text is an effective way to produce task-specific outputs. This enables the zero-shot transfer of our framework to downstream tasks. We study the zero-shot performance of this framework on open information extraction (OIE2016, NYT, WEB, PENN), relation classification (FewRel and TACRED), and factual probe (Google-RE and T-REx). The model transfers non-trivially to most tasks and is often competitive with a fully supervised method without the need for any task-specific training. For instance, we significantly outperform the F1 score of the supervised open information extraction without needing to use its training set.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Information Extraction as a Unified Text-to-Triple Translation
%A Wang, Chenguang
%A Liu, Xiao
%A Chen, Zui
%A Hong, Haoyun
%A Tang, Jie
%A Song, Dawn
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F wang-etal-2021-zero
%X We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize the task as a translation between task-specific input text and output triples. By taking the task-specific input, we enable a task-agnostic translation by leveraging the latent knowledge that a pre-trained language model has about the task. We further demonstrate that a simple pre-training task of predicting which relational information corresponds to which input text is an effective way to produce task-specific outputs. This enables the zero-shot transfer of our framework to downstream tasks. We study the zero-shot performance of this framework on open information extraction (OIE2016, NYT, WEB, PENN), relation classification (FewRel and TACRED), and factual probe (Google-RE and T-REx). The model transfers non-trivially to most tasks and is often competitive with a fully supervised method without the need for any task-specific training. For instance, we significantly outperform the F1 score of the supervised open information extraction without needing to use its training set.
%R 10.18653/v1/2021.emnlp-main.94
%U https://aclanthology.org/2021.emnlp-main.94
%U https://doi.org/10.18653/v1/2021.emnlp-main.94
%P 1225-1238

Markdown (Informal)

[Zero-Shot Information Extraction as a Unified Text-to-Triple Translation](https://aclanthology.org/2021.emnlp-main.94) (Wang et al., EMNLP 2021)

• Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (Wang et al., EMNLP 2021)

ACL

• Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, and Dawn Song. 2021. Zero-Shot Information Extraction as a Unified Text-to-Triple Translation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1225–1238, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.