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        <title>Unsupervised Relation Extraction: A Variational Autoencoder Approach</title>
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    <name type="personal">
        <namePart type="given">Chenhan</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
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            <namePart type="family">Yih</namePart>
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                <placeTerm type="text">Online and Punta Cana, Dominican Republic</placeTerm>
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    <abstract>Unsupervised relation extraction works by clustering entity pairs that have the same relations in the text. Some existing variational autoencoder (VAE)-based approaches train the relation extraction model as an encoder that generates relation classifications. A decoder is trained along with the encoder to reconstruct the encoder input based on the encoder-generated relation classifications. These classifications are a latent variable so they are required to follow a pre-defined prior distribution which results in unstable training. We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable. Specifically, classifications are conditioned on sentence input, while the latent variable is conditioned on both the classifications and the sentence input. This allows our model to connect the decoder with the encoder without putting restrictions on the classification distribution; which improves training stability. Our approach is evaluated on the NYT dataset and outperforms state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.147</identifier>
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        <date>2021-11</date>
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            <start>1929</start>
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%0 Conference Proceedings
%T Unsupervised Relation Extraction: A Variational Autoencoder Approach
%A Yuan, Chenhan
%A Eldardiry, Hoda
%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 yuan-eldardiry-2021-unsupervised
%X Unsupervised relation extraction works by clustering entity pairs that have the same relations in the text. Some existing variational autoencoder (VAE)-based approaches train the relation extraction model as an encoder that generates relation classifications. A decoder is trained along with the encoder to reconstruct the encoder input based on the encoder-generated relation classifications. These classifications are a latent variable so they are required to follow a pre-defined prior distribution which results in unstable training. We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable. Specifically, classifications are conditioned on sentence input, while the latent variable is conditioned on both the classifications and the sentence input. This allows our model to connect the decoder with the encoder without putting restrictions on the classification distribution; which improves training stability. Our approach is evaluated on the NYT dataset and outperforms state-of-the-art methods.
%R 10.18653/v1/2021.emnlp-main.147
%U https://aclanthology.org/2021.emnlp-main.147/
%U https://doi.org/10.18653/v1/2021.emnlp-main.147
%P 1929-1938

Markdown (Informal)

[Unsupervised Relation Extraction: A Variational Autoencoder Approach](https://aclanthology.org/2021.emnlp-main.147/) (Yuan & Eldardiry, EMNLP 2021)

• Unsupervised Relation Extraction: A Variational Autoencoder Approach (Yuan & Eldardiry, EMNLP 2021)

ACL

• Chenhan Yuan and Hoda Eldardiry. 2021. Unsupervised Relation Extraction: A Variational Autoencoder Approach. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1929–1938, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.