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        <title>The Influence of Down-Sampling Strategies on SVD Word Embedding Stability</title>
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        <namePart type="given">Johannes</namePart>
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        <namePart type="family">Kampe</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
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        <name type="personal">
            <namePart type="given">Aleksandr</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Rumshisky</namePart>
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    <abstract>The stability of word embedding algorithms, i.e., the consistency of the word representations they reveal when trained repeatedly on the same data set, has recently raised concerns. We here compare word embedding algorithms on three corpora of different sizes, and evaluate both their stability and accuracy. We find strong evidence that down-sampling strategies (used as part of their training procedures) are particularly influential for the stability of SVD-PPMI-type embeddings. This finding seems to explain diverging reports on their stability and lead us to a simple modification which provides superior stability as well as accuracy on par with skip-gram embedding</abstract>
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%0 Conference Proceedings
%T The Influence of Down-Sampling Strategies on SVD Word Embedding Stability
%A Hellrich, Johannes
%A Kampe, Bernd
%A Hahn, Udo
%Y Rogers, Anna
%Y Drozd, Aleksandr
%Y Rumshisky, Anna
%Y Goldberg, Yoav
%S Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, USA
%F hellrich-etal-2019-influence
%X The stability of word embedding algorithms, i.e., the consistency of the word representations they reveal when trained repeatedly on the same data set, has recently raised concerns. We here compare word embedding algorithms on three corpora of different sizes, and evaluate both their stability and accuracy. We find strong evidence that down-sampling strategies (used as part of their training procedures) are particularly influential for the stability of SVD-PPMI-type embeddings. This finding seems to explain diverging reports on their stability and lead us to a simple modification which provides superior stability as well as accuracy on par with skip-gram embedding
%R 10.18653/v1/W19-2003
%U https://aclanthology.org/W19-2003
%U https://doi.org/10.18653/v1/W19-2003
%P 18-26

Markdown (Informal)

[The Influence of Down-Sampling Strategies on SVD Word Embedding Stability](https://aclanthology.org/W19-2003) (Hellrich et al., RepEval 2019)

• The Influence of Down-Sampling Strategies on SVD Word Embedding Stability (Hellrich et al., RepEval 2019)

ACL

• Johannes Hellrich, Bernd Kampe, and Udo Hahn. 2019. The Influence of Down-Sampling Strategies on SVD Word Embedding Stability. In Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP, pages 18–26, Minneapolis, USA. Association for Computational Linguistics.