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        <title>Improving Relation Extraction with Relational Paraphrase Sentences</title>
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    <name type="personal">
        <namePart type="given">Junjie</namePart>
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    <name type="personal">
        <namePart type="given">Tong</namePart>
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        <namePart type="given">Wenliang</namePart>
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    <name type="personal">
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Supervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit the performance of RE. To increase the coverage of relation expressions, we may enlarge the labeled data by hiring annotators or applying Distant Supervision (DS). However, the human-annotated data is costly and non-scalable while the distantly supervised data contains many noises. In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences. Based on an existing labeled data, we first automatically build a task-specific paraphrase data. Then, we propose a novel model to learn the information of diverse relation expressions. In our model, we try to capture this information on the paraphrases via a joint learning framework. Finally, we conduct experiments on a widely used dataset and the experimental results show that our approach is effective to improve the performance on relation extraction, even compared with a strong baseline.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.148</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T Improving Relation Extraction with Relational Paraphrase Sentences
%A Yu, Junjie
%A Zhu, Tong
%A Chen, Wenliang
%A Zhang, Wei
%A Zhang, Min
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F yu-etal-2020-improving
%X Supervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit the performance of RE. To increase the coverage of relation expressions, we may enlarge the labeled data by hiring annotators or applying Distant Supervision (DS). However, the human-annotated data is costly and non-scalable while the distantly supervised data contains many noises. In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences. Based on an existing labeled data, we first automatically build a task-specific paraphrase data. Then, we propose a novel model to learn the information of diverse relation expressions. In our model, we try to capture this information on the paraphrases via a joint learning framework. Finally, we conduct experiments on a widely used dataset and the experimental results show that our approach is effective to improve the performance on relation extraction, even compared with a strong baseline.
%R 10.18653/v1/2020.coling-main.148
%U https://aclanthology.org/2020.coling-main.148
%U https://doi.org/10.18653/v1/2020.coling-main.148
%P 1687-1698

Markdown (Informal)

[Improving Relation Extraction with Relational Paraphrase Sentences](https://aclanthology.org/2020.coling-main.148) (Yu et al., COLING 2020)

• Improving Relation Extraction with Relational Paraphrase Sentences (Yu et al., COLING 2020)

ACL

• Junjie Yu, Tong Zhu, Wenliang Chen, Wei Zhang, and Min Zhang. 2020. Improving Relation Extraction with Relational Paraphrase Sentences. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1687–1698, Barcelona, Spain (Online). International Committee on Computational Linguistics.