In-game Toxic Language Detection: Shared Task and Attention Residuals
In-game toxic language becomes the hot potato in the gaming industry and community. There have been several online game toxicity analysis frameworks and models proposed. However, it is still challenging to detect toxicity due to the nature of in-game chat, which has extremely short length. In this p...
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creator | Jia, Yuanzhe Wu, Weixuan Cao, Feiqi Han, Soyeon Caren |
description | In-game toxic language becomes the hot potato in the gaming industry and
community. There have been several online game toxicity analysis frameworks and
models proposed. However, it is still challenging to detect toxicity due to the
nature of in-game chat, which has extremely short length. In this paper, we
describe how the in-game toxic language shared task has been established using
the real-world in-game chat data. In addition, we propose and introduce the
model/framework for toxic language token tagging (slot filling) from the
in-game chat. The data and code will be released. |
doi_str_mv | 10.48550/arxiv.2211.05995 |
format | Article |
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community. There have been several online game toxicity analysis frameworks and
models proposed. However, it is still challenging to detect toxicity due to the
nature of in-game chat, which has extremely short length. In this paper, we
describe how the in-game toxic language shared task has been established using
the real-world in-game chat data. In addition, we propose and introduce the
model/framework for toxic language token tagging (slot filling) from the
in-game chat. The data and code will be released.</description><identifier>DOI: 10.48550/arxiv.2211.05995</identifier><language>eng</language><subject>Computer Science - Computation and Language</subject><creationdate>2022-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,777,882</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2211.05995$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2211.05995$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Jia, Yuanzhe</creatorcontrib><creatorcontrib>Wu, Weixuan</creatorcontrib><creatorcontrib>Cao, Feiqi</creatorcontrib><creatorcontrib>Han, Soyeon Caren</creatorcontrib><title>In-game Toxic Language Detection: Shared Task and Attention Residuals</title><description>In-game toxic language becomes the hot potato in the gaming industry and
community. There have been several online game toxicity analysis frameworks and
models proposed. However, it is still challenging to detect toxicity due to the
nature of in-game chat, which has extremely short length. In this paper, we
describe how the in-game toxic language shared task has been established using
the real-world in-game chat data. In addition, we propose and introduce the
model/framework for toxic language token tagging (slot filling) from the
in-game chat. The data and code will be released.</description><subject>Computer Science - Computation and Language</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz7FOwzAUhWEvDKjwAEz4BRJsx_a12apSoFIkJMge3dg3wYK6KElReXto6fQPRzrSx9iNFKV2xog7HA_pu1RKylIY780lW29yMeCWeLM7pMBrzMMeB-IPNFOY0y7f87d3HCnyBqcPjjny5TxTPk78laYU9_g5XbGL_i90fe6CNY_rZvVc1C9Pm9WyLtCCKYCUVSJG0kb5LoBBLRR50L2MJjrU0DnrggcZKutd11kCp0UPEagXGqsFu_2_PTnarzFtcfxpj5725Kl-AQ6SRMM</recordid><startdate>20221110</startdate><enddate>20221110</enddate><creator>Jia, Yuanzhe</creator><creator>Wu, Weixuan</creator><creator>Cao, Feiqi</creator><creator>Han, Soyeon Caren</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20221110</creationdate><title>In-game Toxic Language Detection: Shared Task and Attention Residuals</title><author>Jia, Yuanzhe ; Wu, Weixuan ; Cao, Feiqi ; Han, Soyeon Caren</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-7e2620dde4529bc75a402e974f1d5d8a47b868c971c3698bb6e7840f7d7ef04a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Computation and Language</topic><toplevel>online_resources</toplevel><creatorcontrib>Jia, Yuanzhe</creatorcontrib><creatorcontrib>Wu, Weixuan</creatorcontrib><creatorcontrib>Cao, Feiqi</creatorcontrib><creatorcontrib>Han, Soyeon Caren</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jia, Yuanzhe</au><au>Wu, Weixuan</au><au>Cao, Feiqi</au><au>Han, Soyeon Caren</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>In-game Toxic Language Detection: Shared Task and Attention Residuals</atitle><date>2022-11-10</date><risdate>2022</risdate><abstract>In-game toxic language becomes the hot potato in the gaming industry and
community. There have been several online game toxicity analysis frameworks and
models proposed. However, it is still challenging to detect toxicity due to the
nature of in-game chat, which has extremely short length. In this paper, we
describe how the in-game toxic language shared task has been established using
the real-world in-game chat data. In addition, we propose and introduce the
model/framework for toxic language token tagging (slot filling) from the
in-game chat. The data and code will be released.</abstract><doi>10.48550/arxiv.2211.05995</doi><oa>free_for_read</oa></addata></record> |
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title | In-game Toxic Language Detection: Shared Task and Attention Residuals |
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