Related papers: Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi

Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi

URL: http://arxiv.org/abs/2211.10163v1
Date: Fri, 18 Nov 2022 11:17:15 GMT
Title: Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi
Authors: Tharindu Ranasinghe, Kai North, Damith Premasiri, Marcos Zampieri
Abstract summary: The HASOC (Hate Speech and Offensive Content Identification) shared task is one of these initiatives. In its fourth iteration, HASOC 2022 included three subtracks for English, Hindi, and Marathi. We report the results of the HASOC 2022 Marathi subtrack which provided participants with a dataset containing data from Twitter manually annotated using the popular OLID taxonomy. The best performing algorithms were a mixture of traditional and deep learning approaches.
Score: 15.466844451996051
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The widespread of offensive content online has become a reason for great concern in recent years, motivating researchers to develop robust systems capable of identifying such content automatically. With the goal of carrying out a fair evaluation of these systems, several international competitions have been organized, providing the community with important benchmark data and evaluation methods for various languages. Organized since 2019, the HASOC (Hate Speech and Offensive Content Identification) shared task is one of these initiatives. In its fourth iteration, HASOC 2022 included three subtracks for English, Hindi, and Marathi. In this paper, we report the results of the HASOC 2022 Marathi subtrack which provided participants with a dataset containing data from Twitter manually annotated using the popular OLID taxonomy. The Marathi track featured three additional subtracks, each corresponding to one level of the taxonomy: Task A - offensive content identification (offensive vs. non-offensive); Task B - categorization of offensive types (targeted vs. untargeted), and Task C - offensive target identification (individual vs. group vs. others). Overall, 59 runs were submitted by 10 teams. The best systems obtained an F1 of 0.9745 for Subtrack 3A, an F1 of 0.9207 for Subtrack 3B, and F1 of 0.9607 for Subtrack 3C. The best performing algorithms were a mixture of traditional and deep learning approaches.

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