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        <title>Zero-shot Relation Classification as Textual Entailment</title>
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            <namePart type="given">James</namePart>
            <namePart type="family">Thorne</namePart>
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            <namePart type="given">Oana</namePart>
            <namePart type="family">Cocarascu</namePart>
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    <abstract>We consider the task of relation classification, and pose this task as one of textual entailment. We show that this formulation leads to several advantages, including the ability to (i) perform zero-shot relation classification by exploiting relation descriptions, (ii) utilize existing textual entailment models, and (iii) leverage readily available textual entailment datasets, to enhance the performance of relation classification systems. Our experiments show that the proposed approach achieves 20.16% and 61.32% in F1 zero-shot classification performance on two datasets, which further improved to 22.80% and 64.78% respectively with the use of conditional encoding.</abstract>
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%0 Conference Proceedings
%T Zero-shot Relation Classification as Textual Entailment
%A Obamuyide, Abiola
%A Vlachos, Andreas
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F obamuyide-vlachos-2018-zero
%X We consider the task of relation classification, and pose this task as one of textual entailment. We show that this formulation leads to several advantages, including the ability to (i) perform zero-shot relation classification by exploiting relation descriptions, (ii) utilize existing textual entailment models, and (iii) leverage readily available textual entailment datasets, to enhance the performance of relation classification systems. Our experiments show that the proposed approach achieves 20.16% and 61.32% in F1 zero-shot classification performance on two datasets, which further improved to 22.80% and 64.78% respectively with the use of conditional encoding.
%R 10.18653/v1/W18-5511
%U https://aclanthology.org/W18-5511
%U https://doi.org/10.18653/v1/W18-5511
%P 72-78

Markdown (Informal)

[Zero-shot Relation Classification as Textual Entailment](https://aclanthology.org/W18-5511) (Obamuyide & Vlachos, EMNLP 2018)

• Zero-shot Relation Classification as Textual Entailment (Obamuyide & Vlachos, EMNLP 2018)

ACL

• Abiola Obamuyide and Andreas Vlachos. 2018. Zero-shot Relation Classification as Textual Entailment. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 72–78, Brussels, Belgium. Association for Computational Linguistics.