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        <title>Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>We present InstaMap, an instance-based method for learning projection-based cross-lingual word embeddings. Unlike prior work, it deviates from learning a single global linear projection. InstaMap is a non-parametric model that learns a non-linear projection by iteratively: (1) finding a globally optimal rotation of the source embedding space relying on the Kabsch algorithm, and then (2) moving each point along an instance-specific translation vector estimated from the translation vectors of the point’s nearest neighbours in the training dictionary. We report performance gains with InstaMap over four representative state-of-the-art projection-based models on bilingual lexicon induction across a set of 28 diverse language pairs. We note prominent improvements, especially for more distant language pairs (i.e., languages with non-isomorphic monolingual spaces).</abstract>
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%0 Conference Proceedings
%T Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces
%A Glavaš, Goran
%A Vulić, Ivan
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F glavas-vulic-2020-non
%X We present InstaMap, an instance-based method for learning projection-based cross-lingual word embeddings. Unlike prior work, it deviates from learning a single global linear projection. InstaMap is a non-parametric model that learns a non-linear projection by iteratively: (1) finding a globally optimal rotation of the source embedding space relying on the Kabsch algorithm, and then (2) moving each point along an instance-specific translation vector estimated from the translation vectors of the point’s nearest neighbours in the training dictionary. We report performance gains with InstaMap over four representative state-of-the-art projection-based models on bilingual lexicon induction across a set of 28 diverse language pairs. We note prominent improvements, especially for more distant language pairs (i.e., languages with non-isomorphic monolingual spaces).
%R 10.18653/v1/2020.acl-main.675
%U https://aclanthology.org/2020.acl-main.675
%U https://doi.org/10.18653/v1/2020.acl-main.675
%P 7548-7555

Markdown (Informal)

[Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces](https://aclanthology.org/2020.acl-main.675) (Glavaš & Vulić, ACL 2020)

• Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces (Glavaš & Vulić, ACL 2020)

ACL

• Goran Glavaš and Ivan Vulić. 2020. Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7548–7555, Online. Association for Computational Linguistics.