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        <title>Detect Profane Language in Streaming Services to Protect Young Audiences</title>
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        <namePart type="given">Jingxiang</namePart>
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    <name type="personal">
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            <title>Proceedings of the 4th Workshop on e-Commerce and NLP</title>
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            <namePart type="given">Surya</namePart>
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    <abstract>With the rapid growth of online video streaming, recent years have seen increasing concerns about profane language in their content. Detecting profane language in streaming services is challenging due to the long sentences appeared in a video. While recent research on handling long sentences has focused on developing deep learning modeling techniques, little work has focused on techniques on improving data pipelines. In this work, we develop a data collection pipeline to address long sequence of texts and integrate this pipeline with a multi-head self-attention model. With this pipeline, our experiments show the self-attention model offers 12.5% relative accuracy improvement over state-of-the-art distilBERT model on profane language detection while requiring only 3% of parameters. This research designs a better system for informing users of profane language in video streaming services.</abstract>
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    <identifier type="doi">10.18653/v1/2021.ecnlp-1.15</identifier>
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%0 Conference Proceedings
%T Detect Profane Language in Streaming Services to Protect Young Audiences
%A Chen, Jingxiang
%A Wei, Kai
%A Hao, Xiang
%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 chen-etal-2021-detect
%X With the rapid growth of online video streaming, recent years have seen increasing concerns about profane language in their content. Detecting profane language in streaming services is challenging due to the long sentences appeared in a video. While recent research on handling long sentences has focused on developing deep learning modeling techniques, little work has focused on techniques on improving data pipelines. In this work, we develop a data collection pipeline to address long sequence of texts and integrate this pipeline with a multi-head self-attention model. With this pipeline, our experiments show the self-attention model offers 12.5% relative accuracy improvement over state-of-the-art distilBERT model on profane language detection while requiring only 3% of parameters. This research designs a better system for informing users of profane language in video streaming services.
%R 10.18653/v1/2021.ecnlp-1.15
%U https://aclanthology.org/2021.ecnlp-1.15
%U https://doi.org/10.18653/v1/2021.ecnlp-1.15
%P 123-131

Markdown (Informal)

[Detect Profane Language in Streaming Services to Protect Young Audiences](https://aclanthology.org/2021.ecnlp-1.15) (Chen et al., ECNLP 2021)

• Detect Profane Language in Streaming Services to Protect Young Audiences (Chen et al., ECNLP 2021)

ACL

• Jingxiang Chen, Kai Wei, and Xiang Hao. 2021. Detect Profane Language in Streaming Services to Protect Young Audiences. In Proceedings of the 4th Workshop on e-Commerce and NLP, pages 123–131, Online. Association for Computational Linguistics.