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        <title>Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop</title>
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        <namePart type="given">Rujun</namePart>
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    <name type="personal">
        <namePart type="given">Kyunghyun</namePart>
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    <abstract>Conventional word embedding models do not leverage information from document meta-data, and they do not model uncertainty. We address these concerns with a model that incorporates document covariates to estimate conditional word embedding distributions. Our model allows for (a) hypothesis tests about the meanings of terms, (b) assessments as to whether a word is near or far from another conditioned on different covariate values, and (c) assessments as to whether estimated differences are statistically significant.</abstract>
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%0 Conference Proceedings
%T Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop
%A Han, Rujun
%A Gill, Michael
%A Spirling, Arthur
%A Cho, Kyunghyun
%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 han-etal-2018-conditional
%X Conventional word embedding models do not leverage information from document meta-data, and they do not model uncertainty. We address these concerns with a model that incorporates document covariates to estimate conditional word embedding distributions. Our model allows for (a) hypothesis tests about the meanings of terms, (b) assessments as to whether a word is near or far from another conditioned on different covariate values, and (c) assessments as to whether estimated differences are statistically significant.
%R 10.18653/v1/D18-1527
%U https://aclanthology.org/D18-1527/
%U https://doi.org/10.18653/v1/D18-1527
%P 4890-4895

Markdown (Informal)

[Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop](https://aclanthology.org/D18-1527/) (Han et al., EMNLP 2018)

• Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop (Han et al., EMNLP 2018)

ACL

• Rujun Han, Michael Gill, Arthur Spirling, and Kyunghyun Cho. 2018. Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4890–4895, Brussels, Belgium. Association for Computational Linguistics.