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New model enhances bilingual lexicon induction with latent variables

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This paper introduces a new discriminative latent variable model designed for bilingual lexicon induction. The model integrates a bipartite matching dictionary prior with a representation-based approach. Researchers developed an efficient Viterbi EM algorithm for training and demonstrated improved bilingual lexicons across six language pairs and two metrics, also showing how prior work can be viewed through a similar latent-variable model lens. AI

RANK_REASON The item is an academic paper published on arXiv detailing a new model for bilingual lexicon induction. [lever_c_demoted from research: ic=1 ai=1.0]

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New model enhances bilingual lexicon induction with latent variables

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The item is an academic paper published on arXiv detailing a new model for bilingual lexicon induction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Sebastian Ruder, Ryan Cotterell, Yova Kementchedjhieva, Anders S{\o}gaard ·

    A Discriminative Latent-Variable Model for Bilingual Lexicon Induction

    arXiv:1808.09334v4 Announce Type: replace-cross Abstract: We introduce a novel discriminative latent variable model for bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a representation-based approach (Artetxe …

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