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        <title>Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
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            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amount of data available across languages. In this paper we address this issue and propose Conception, a novel technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. Our approach results in high-coverage representations that outperform the state of the art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation, proving particularly robust on low-resource languages. Conception – its software and the complete set of representations – is available at https://github.com/SapienzaNLP/conception.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.291</identifier>
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%0 Conference Proceedings
%T Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations
%A Conia, Simone
%A Navigli, Roberto
%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 conia-navigli-2020-conception
%X To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amount of data available across languages. In this paper we address this issue and propose Conception, a novel technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. Our approach results in high-coverage representations that outperform the state of the art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation, proving particularly robust on low-resource languages. Conception – its software and the complete set of representations – is available at https://github.com/SapienzaNLP/conception.
%R 10.18653/v1/2020.coling-main.291
%U https://aclanthology.org/2020.coling-main.291
%U https://doi.org/10.18653/v1/2020.coling-main.291
%P 3268-3284

Markdown (Informal)

[Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations](https://aclanthology.org/2020.coling-main.291) (Conia & Navigli, COLING 2020)

• Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations (Conia & Navigli, COLING 2020)

ACL

• Simone Conia and Roberto Navigli. 2020. Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3268–3284, Barcelona, Spain (Online). International Committee on Computational Linguistics.