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Overview of the HASOC subtrack at FIRE 2021: Hate speech and offensive content identification in English and Indo-Aryan languages and conversational hate speech
Mandl, Thomas Modha, Sandip Shahi, Gautam Kishore Madhu, Hiren Satapara, Shrey Majumder, Prasenjit Schäfer, Johannes Ranasinghe, Tharindu Nandini, Durgesh
Mandl, Thomas
Modha, Sandip
Shahi, Gautam Kishore
Madhu, Hiren
Satapara, Shrey
Majumder, Prasenjit
Schäfer, Johannes
Ranasinghe, Tharindu
Nandini, Durgesh
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2021-12-13
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Abstract
The HASOC track is dedicated to the evaluation of technology for finding Offensive Language and Hate Speech. HASOC is creating a multilingual data corpus mainly for English and under-resourced languages(Hindi and Marathi). This paper presents one HASOC subtrack with two tasks. In 2021, we organized the classification task for English, Hindi, and Marathi. The first task consists of two classification tasks; Subtask 1A consists of a binary and fine-grained classification into offensive and non-offensive tweets. Subtask 1B asks to classify the tweets into Hate, Profane and offensive. Task 2 consists of identifying tweets given additional context in the form of the preceding conversion. During the shared task, 65 teams have submitted 652 runs. This overview paper briefly presents the task descriptions, the data and the results obtained from the participant's submission.
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Mandl, T., Modha, S., Shahi, G.K. et al. (2021) Overview of the HASOC subtrack at FIRE 2021: Hate speech and offensive content identification in English and Indo-Aryan languages and conversational hate speech. FIRE 2021: Forum for Information Retrieval Evaluation, Virtual Event India December 13th-17th, 2021. Pages 1–3 https://doi.org/10.1145/3503162.3503176
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en
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This is an accepted manuscript of a paper published by ACM in the proceedings of FIRE 2021: Forum for Information Retrieval Evaluation on 13/12/2021, available online: https://doi.org/10.1145/3503162.3503176 The accepted manuscript of the publication may differ from the final published version.
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9781450395960