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        <title>Reconstruction of Word Embeddings from Sub-Word Parameters</title>
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            <title>Proceedings of the First Workshop on Subword and Character Level Models in NLP</title>
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        <name type="personal">
            <namePart type="given">Manaal</namePart>
            <namePart type="family">Faruqui</namePart>
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        <name type="personal">
            <namePart type="given">Hinrich</namePart>
            <namePart type="family">Schuetze</namePart>
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        <name type="personal">
            <namePart type="given">Isabel</namePart>
            <namePart type="family">Trancoso</namePart>
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        <name type="personal">
            <namePart type="given">Yadollah</namePart>
            <namePart type="family">Yaghoobzadeh</namePart>
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    <abstract>Pre-trained word embeddings improve the performance of a neural model at the cost of increasing the model size. We propose to benefit from this resource without paying the cost by operating strictly at the sub-lexical level. Our approach is quite simple: before task-specific training, we first optimize sub-word parameters to reconstruct pre-trained word embeddings using various distance measures. We report interesting results on a variety of tasks: word similarity, word analogy, and part-of-speech tagging.</abstract>
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    <identifier type="doi">10.18653/v1/W17-4119</identifier>
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%0 Conference Proceedings
%T Reconstruction of Word Embeddings from Sub-Word Parameters
%A Stratos, Karl
%Y Faruqui, Manaal
%Y Schuetze, Hinrich
%Y Trancoso, Isabel
%Y Yaghoobzadeh, Yadollah
%S Proceedings of the First Workshop on Subword and Character Level Models in NLP
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F stratos-2017-reconstruction
%X Pre-trained word embeddings improve the performance of a neural model at the cost of increasing the model size. We propose to benefit from this resource without paying the cost by operating strictly at the sub-lexical level. Our approach is quite simple: before task-specific training, we first optimize sub-word parameters to reconstruct pre-trained word embeddings using various distance measures. We report interesting results on a variety of tasks: word similarity, word analogy, and part-of-speech tagging.
%R 10.18653/v1/W17-4119
%U https://aclanthology.org/W17-4119
%U https://doi.org/10.18653/v1/W17-4119
%P 130-135

Markdown (Informal)

[Reconstruction of Word Embeddings from Sub-Word Parameters](https://aclanthology.org/W17-4119) (Stratos, SCLeM 2017)

• Reconstruction of Word Embeddings from Sub-Word Parameters (Stratos, SCLeM 2017)

ACL

• Karl Stratos. 2017. Reconstruction of Word Embeddings from Sub-Word Parameters. In Proceedings of the First Workshop on Subword and Character Level Models in NLP, pages 130–135, Copenhagen, Denmark. Association for Computational Linguistics.