@inproceedings{ruder-etal-2018-discriminative,
title = "A Discriminative Latent-Variable Model for Bilingual Lexicon Induction",
author = "Ruder, Sebastian and
Cotterell, Ryan and
Kementchedjhieva, Yova and
S{\o}gaard, Anders",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1042",
doi = "10.18653/v1/D18-1042",
pages = "458--468",
abstract = "We introduce a novel discriminative latent-variable model for the task of bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a state-of-the-art embedding-based approach. To train the model, we derive an efficient Viterbi EM algorithm. We provide empirical improvements on six language pairs under two metrics and show that the prior theoretically and empirically helps to mitigate the hubness problem. We also demonstrate how previous work may be viewed as a similarly fashioned latent-variable model, albeit with a different prior.",
}
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<abstract>We introduce a novel discriminative latent-variable model for the task of bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a state-of-the-art embedding-based approach. To train the model, we derive an efficient Viterbi EM algorithm. We provide empirical improvements on six language pairs under two metrics and show that the prior theoretically and empirically helps to mitigate the hubness problem. We also demonstrate how previous work may be viewed as a similarly fashioned latent-variable model, albeit with a different prior.</abstract>
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%0 Conference Proceedings
%T A Discriminative Latent-Variable Model for Bilingual Lexicon Induction
%A Ruder, Sebastian
%A Cotterell, Ryan
%A Kementchedjhieva, Yova
%A Søgaard, Anders
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ruder-etal-2018-discriminative
%X We introduce a novel discriminative latent-variable model for the task of bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a state-of-the-art embedding-based approach. To train the model, we derive an efficient Viterbi EM algorithm. We provide empirical improvements on six language pairs under two metrics and show that the prior theoretically and empirically helps to mitigate the hubness problem. We also demonstrate how previous work may be viewed as a similarly fashioned latent-variable model, albeit with a different prior.
%R 10.18653/v1/D18-1042
%U https://aclanthology.org/D18-1042
%U https://doi.org/10.18653/v1/D18-1042
%P 458-468
Markdown (Informal)
[A Discriminative Latent-Variable Model for Bilingual Lexicon Induction](https://aclanthology.org/D18-1042) (Ruder et al., EMNLP 2018)
ACL