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        <title>Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes</title>
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            <namePart type="given">Mirella</namePart>
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    <abstract>We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm to learn a large translation table without any parallel text or seed lexicon. First, we solve the memory bottleneck and enforce the sparsity with a simple thresholding scheme for the lexicon. Second, we initialize the lexicon training with word classes, which efficiently boosts the performance. Our methods produced promising results on two large-scale unsupervised translation tasks.</abstract>
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%0 Conference Proceedings
%T Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes
%A Kim, Yunsu
%A Schamper, Julian
%A Ney, Hermann
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F kim-etal-2017-unsupervised
%X We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm to learn a large translation table without any parallel text or seed lexicon. First, we solve the memory bottleneck and enforce the sparsity with a simple thresholding scheme for the lexicon. Second, we initialize the lexicon training with word classes, which efficiently boosts the performance. Our methods produced promising results on two large-scale unsupervised translation tasks.
%U https://aclanthology.org/E17-2103
%P 650-656

Markdown (Informal)

[Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes](https://aclanthology.org/E17-2103) (Kim et al., EACL 2017)

• Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes (Kim et al., EACL 2017)

ACL

• Yunsu Kim, Julian Schamper, and Hermann Ney. 2017. Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 650–656, Valencia, Spain. Association for Computational Linguistics.