@inproceedings{winkler-etal-2021-exploring,
title = "Exploring Inspiration Sets in a Data Programming Pipeline for Product Moderation",
author = "Winkler, Justine and
Brugman, Simon and
van Berkel, Bas and
Larson, Martha",
editor = "Malmasi, Shervin and
Kallumadi, Surya and
Ueffing, Nicola and
Rokhlenko, Oleg and
Agichtein, Eugene and
Guy, Ido",
booktitle = "Proceedings of the 4th Workshop on e-Commerce and NLP",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.ecnlp-1.16",
doi = "10.18653/v1/2021.ecnlp-1.16",
pages = "132--139",
abstract = "We carry out a case study on the use of data programming to create data to train classifiers used for product moderation on a large e-commerce platform. Data programming is a recently-introduced technique that uses human-defined rules to generate training data sets without tedious item-by-item hand labeling. Our study investigates methods for allowing product moderators to quickly modify the rules given their knowledge of the domain and, especially, of textual item descriptions. Our results show promise that moderators can use this approach to steer the training data, making possible fast and close control of classifiers that detect policy violations.",
}
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<abstract>We carry out a case study on the use of data programming to create data to train classifiers used for product moderation on a large e-commerce platform. Data programming is a recently-introduced technique that uses human-defined rules to generate training data sets without tedious item-by-item hand labeling. Our study investigates methods for allowing product moderators to quickly modify the rules given their knowledge of the domain and, especially, of textual item descriptions. Our results show promise that moderators can use this approach to steer the training data, making possible fast and close control of classifiers that detect policy violations.</abstract>
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%0 Conference Proceedings
%T Exploring Inspiration Sets in a Data Programming Pipeline for Product Moderation
%A Winkler, Justine
%A Brugman, Simon
%A van Berkel, Bas
%A Larson, Martha
%Y Malmasi, Shervin
%Y Kallumadi, Surya
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the 4th Workshop on e-Commerce and NLP
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F winkler-etal-2021-exploring
%X We carry out a case study on the use of data programming to create data to train classifiers used for product moderation on a large e-commerce platform. Data programming is a recently-introduced technique that uses human-defined rules to generate training data sets without tedious item-by-item hand labeling. Our study investigates methods for allowing product moderators to quickly modify the rules given their knowledge of the domain and, especially, of textual item descriptions. Our results show promise that moderators can use this approach to steer the training data, making possible fast and close control of classifiers that detect policy violations.
%R 10.18653/v1/2021.ecnlp-1.16
%U https://aclanthology.org/2021.ecnlp-1.16
%U https://doi.org/10.18653/v1/2021.ecnlp-1.16
%P 132-139
Markdown (Informal)
[Exploring Inspiration Sets in a Data Programming Pipeline for Product Moderation](https://aclanthology.org/2021.ecnlp-1.16) (Winkler et al., ECNLP 2021)
ACL