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    <abstract>Zero-shot relation extraction (ZSRE) aims to predict target relations that cannot be observed during training. While most previous studies have focused on fully supervised relation extraction and achieved considerably high performance, less effort has been made towards ZSRE. This study proposes a new model incorporating discriminative embedding learning for both sentences and semantic relations. In addition, a self-adaptive comparator network is used to judge whether the relationship between a sentence and a relation is consistent. Experimental results on two benchmark datasets showed that the proposed method significantly outperforms the state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Improving Discriminative Learning for Zero-Shot Relation Extraction
%A Tran, Van-Hien
%A Ouchi, Hiroki
%A Watanabe, Taro
%A Matsumoto, Yuji
%Y Das, Rajarshi
%Y Lewis, Patrick
%Y Min, Sewon
%Y Thai, June
%Y Zaheer, Manzil
%S Proceedings of the 1st Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland and Online
%F tran-etal-2022-improving
%X Zero-shot relation extraction (ZSRE) aims to predict target relations that cannot be observed during training. While most previous studies have focused on fully supervised relation extraction and achieved considerably high performance, less effort has been made towards ZSRE. This study proposes a new model incorporating discriminative embedding learning for both sentences and semantic relations. In addition, a self-adaptive comparator network is used to judge whether the relationship between a sentence and a relation is consistent. Experimental results on two benchmark datasets showed that the proposed method significantly outperforms the state-of-the-art methods.
%R 10.18653/v1/2022.spanlp-1.1
%U https://aclanthology.org/2022.spanlp-1.1
%U https://doi.org/10.18653/v1/2022.spanlp-1.1
%P 1-6

Markdown (Informal)

[Improving Discriminative Learning for Zero-Shot Relation Extraction](https://aclanthology.org/2022.spanlp-1.1) (Tran et al., SpaNLP 2022)

• Improving Discriminative Learning for Zero-Shot Relation Extraction (Tran et al., SpaNLP 2022)

ACL

• Van-Hien Tran, Hiroki Ouchi, Taro Watanabe, and Yuji Matsumoto. 2022. Improving Discriminative Learning for Zero-Shot Relation Extraction. In Proceedings of the 1st Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge, pages 1–6, Dublin, Ireland and Online. Association for Computational Linguistics.