@inproceedings{hu-2018-somm,
title = "{S}omm: Into the Model",
author = "Hu, Shengli",
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/D18-1146",
doi = "10.18653/v1/D18-1146",
pages = "1153--1159",
abstract = "To what extent could the sommelier profession, or wine stewardship, be displaced by machine leaning algorithms? There are at least three essential skills that make a qualified sommelier: wine theory, blind tasting, and beverage service, as exemplified in the rigorous certification processes of certified sommeliers and above (advanced and master) with the most authoritative body in the industry, the Court of Master Sommelier (hereafter CMS). We propose and train corresponding machine learning models that match these skills, and compare algorithmic results with real data collected from a large group of wine professionals. We find that our machine learning models outperform human sommeliers on most tasks {---} most notably in the section of blind tasting, where hierarchically supervised Latent Dirichlet Allocation outperforms sommeliers{'} judgment calls by over 6{\%} in terms of F1-score; and in the section of beverage service, especially wine and food pairing, a modified Siamese neural network based on BiLSTM achieves better results than sommeliers by 2{\%}. This demonstrates, contrary to popular opinion in the industry, that the sommelier profession is at least to some extent automatable, barring economic (Kleinberg et al., 2017) and psychological (Dietvorst et al., 2015) complications.",
}
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<abstract>To what extent could the sommelier profession, or wine stewardship, be displaced by machine leaning algorithms? There are at least three essential skills that make a qualified sommelier: wine theory, blind tasting, and beverage service, as exemplified in the rigorous certification processes of certified sommeliers and above (advanced and master) with the most authoritative body in the industry, the Court of Master Sommelier (hereafter CMS). We propose and train corresponding machine learning models that match these skills, and compare algorithmic results with real data collected from a large group of wine professionals. We find that our machine learning models outperform human sommeliers on most tasks — most notably in the section of blind tasting, where hierarchically supervised Latent Dirichlet Allocation outperforms sommeliers’ judgment calls by over 6% in terms of F1-score; and in the section of beverage service, especially wine and food pairing, a modified Siamese neural network based on BiLSTM achieves better results than sommeliers by 2%. This demonstrates, contrary to popular opinion in the industry, that the sommelier profession is at least to some extent automatable, barring economic (Kleinberg et al., 2017) and psychological (Dietvorst et al., 2015) complications.</abstract>
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%0 Conference Proceedings
%T Somm: Into the Model
%A Hu, Shengli
%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 hu-2018-somm
%X To what extent could the sommelier profession, or wine stewardship, be displaced by machine leaning algorithms? There are at least three essential skills that make a qualified sommelier: wine theory, blind tasting, and beverage service, as exemplified in the rigorous certification processes of certified sommeliers and above (advanced and master) with the most authoritative body in the industry, the Court of Master Sommelier (hereafter CMS). We propose and train corresponding machine learning models that match these skills, and compare algorithmic results with real data collected from a large group of wine professionals. We find that our machine learning models outperform human sommeliers on most tasks — most notably in the section of blind tasting, where hierarchically supervised Latent Dirichlet Allocation outperforms sommeliers’ judgment calls by over 6% in terms of F1-score; and in the section of beverage service, especially wine and food pairing, a modified Siamese neural network based on BiLSTM achieves better results than sommeliers by 2%. This demonstrates, contrary to popular opinion in the industry, that the sommelier profession is at least to some extent automatable, barring economic (Kleinberg et al., 2017) and psychological (Dietvorst et al., 2015) complications.
%R 10.18653/v1/D18-1146
%U https://aclanthology.org/D18-1146
%U https://doi.org/10.18653/v1/D18-1146
%P 1153-1159
Markdown (Informal)
[Somm: Into the Model](https://aclanthology.org/D18-1146) (Hu, EMNLP 2018)
ACL
- Shengli Hu. 2018. Somm: Into the Model. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1153–1159, Brussels, Belgium. Association for Computational Linguistics.