SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers
This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise in social media platforms has seen an increase in disproportionate levels of sexism experienced by women on social media platforms. This has made...
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| creator | Rallabandi, Sriya Singhal, Sanchit Seth, Pratinav |
| description | This paper describes our submission to Task 10 at SemEval 2023-Explainable
Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise
in social media platforms has seen an increase in disproportionate levels of
sexism experienced by women on social media platforms. This has made detecting
and explaining online sexist content more important than ever to make social
media safer and more accessible for women. Our approach consists of
experimenting and finetuning BERT-based models and using a Majority Voting
ensemble model that outperforms individual baseline model scores. Our system
achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319
for Task C. |
| doi_str_mv | 10.48550/arxiv.2304.03518 |
| format | Article |
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Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise
in social media platforms has seen an increase in disproportionate levels of
sexism experienced by women on social media platforms. This has made detecting
and explaining online sexist content more important than ever to make social
media safer and more accessible for women. Our approach consists of
experimenting and finetuning BERT-based models and using a Majority Voting
ensemble model that outperforms individual baseline model scores. Our system
achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319
for Task C.</description><identifier>DOI: 10.48550/arxiv.2304.03518</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Computers and Society ; Computer Science - Learning</subject><creationdate>2023-04</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>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2304.03518$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2304.03518$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Rallabandi, Sriya</creatorcontrib><creatorcontrib>Singhal, Sanchit</creatorcontrib><creatorcontrib>Seth, Pratinav</creatorcontrib><title>SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers</title><description>This paper describes our submission to Task 10 at SemEval 2023-Explainable
Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise
in social media platforms has seen an increase in disproportionate levels of
sexism experienced by women on social media platforms. This has made detecting
and explaining online sexist content more important than ever to make social
media safer and more accessible for women. Our approach consists of
experimenting and finetuning BERT-based models and using a Majority Voting
ensemble model that outperforms individual baseline model scores. Our system
achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319
for Task C.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Computers and Society</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotjzFPwzAUhL0woMIPYMJ_IOE9O04TNlRaQCrqkIg1eokdZEicyE6r9N8TCsvdDXcnfYzdIcRJphQ8kJ_tKRYSkhikwuyajUVRcJp4YfrtibpIgJC8pPDNER75dh47so7qzvBnM5lmsoPjQ8sPrrPOLKvZhp4fg3Wf_J2-Bm-nM_8YJqP5bilE5dEtsfTkQjv43vhww65a6oK5_fcVK3fbcvMa7Q8vb5unfUTpOotqJYxaQ61kDokiQNSgESgHYagxdWqwzXWNIksxEbmUi2CDKFONOk9Rrtj93-0FuRq97cmfq1_06oIufwCZUlIa</recordid><startdate>20230407</startdate><enddate>20230407</enddate><creator>Rallabandi, Sriya</creator><creator>Singhal, Sanchit</creator><creator>Seth, Pratinav</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230407</creationdate><title>SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers</title><author>Rallabandi, Sriya ; Singhal, Sanchit ; Seth, Pratinav</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a678-b52e570b539045a011d0d10a902eaceb6e1f9db12861429334291c1136d1d9613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Computers and Society</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Rallabandi, Sriya</creatorcontrib><creatorcontrib>Singhal, Sanchit</creatorcontrib><creatorcontrib>Seth, Pratinav</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Rallabandi, Sriya</au><au>Singhal, Sanchit</au><au>Seth, Pratinav</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers</atitle><date>2023-04-07</date><risdate>2023</risdate><abstract>This paper describes our submission to Task 10 at SemEval 2023-Explainable
Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise
in social media platforms has seen an increase in disproportionate levels of
sexism experienced by women on social media platforms. This has made detecting
and explaining online sexist content more important than ever to make social
media safer and more accessible for women. Our approach consists of
experimenting and finetuning BERT-based models and using a Majority Voting
ensemble model that outperforms individual baseline model scores. Our system
achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319
for Task C.</abstract><doi>10.48550/arxiv.2304.03518</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Computers and Society Computer Science - Learning |
| title | SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers |
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