@inproceedings{laud-etal-2020-problemconquero,
title = "problem{C}onquero at {S}em{E}val-2020 Task 12: Transformer and Soft Label-based Approaches",
author = "Laud, Karishma and
Singh, Jagriti and
Sahu, Randeep Kumar and
Modi, Ashutosh",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.282/",
doi = "10.18653/v1/2020.semeval-1.282",
pages = "2123--2132",
abstract = "In this paper, we present various systems submitted by our team problemConquero for SemEval-2020 Shared Task 12 {\textquotedblleft}Multilingual Offensive Language Identification in Social Media{\textquotedblright}. We participated in all the three sub-tasks of OffensEval-2020, and our final submissions during the evaluation phase included transformer-based approaches and a soft label-based approach. BERT based fine-tuned models were submitted for each language of sub-task A (offensive tweet identification). RoBERTa based fine-tuned model for sub-task B (automatic categorization of offense types) was submitted. We submitted two models for sub-task C (offense target identification), one using soft labels and the other using BERT based fine-tuned model. Our ranks for sub-task A were Greek-19 out of 37, Turkish-22 out of 46, Danish-26 out of 39, Arabic-39 out of 53, and English-20 out of 85. We achieved a rank of 28 out of 43 for sub-task B. Our best rank for sub-task C was 20 out of 39 using BERT based fine-tuned model."
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<abstract>In this paper, we present various systems submitted by our team problemConquero for SemEval-2020 Shared Task 12 “Multilingual Offensive Language Identification in Social Media”. We participated in all the three sub-tasks of OffensEval-2020, and our final submissions during the evaluation phase included transformer-based approaches and a soft label-based approach. BERT based fine-tuned models were submitted for each language of sub-task A (offensive tweet identification). RoBERTa based fine-tuned model for sub-task B (automatic categorization of offense types) was submitted. We submitted two models for sub-task C (offense target identification), one using soft labels and the other using BERT based fine-tuned model. Our ranks for sub-task A were Greek-19 out of 37, Turkish-22 out of 46, Danish-26 out of 39, Arabic-39 out of 53, and English-20 out of 85. We achieved a rank of 28 out of 43 for sub-task B. Our best rank for sub-task C was 20 out of 39 using BERT based fine-tuned model.</abstract>
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%0 Conference Proceedings
%T problemConquero at SemEval-2020 Task 12: Transformer and Soft Label-based Approaches
%A Laud, Karishma
%A Singh, Jagriti
%A Sahu, Randeep Kumar
%A Modi, Ashutosh
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F laud-etal-2020-problemconquero
%X In this paper, we present various systems submitted by our team problemConquero for SemEval-2020 Shared Task 12 “Multilingual Offensive Language Identification in Social Media”. We participated in all the three sub-tasks of OffensEval-2020, and our final submissions during the evaluation phase included transformer-based approaches and a soft label-based approach. BERT based fine-tuned models were submitted for each language of sub-task A (offensive tweet identification). RoBERTa based fine-tuned model for sub-task B (automatic categorization of offense types) was submitted. We submitted two models for sub-task C (offense target identification), one using soft labels and the other using BERT based fine-tuned model. Our ranks for sub-task A were Greek-19 out of 37, Turkish-22 out of 46, Danish-26 out of 39, Arabic-39 out of 53, and English-20 out of 85. We achieved a rank of 28 out of 43 for sub-task B. Our best rank for sub-task C was 20 out of 39 using BERT based fine-tuned model.
%R 10.18653/v1/2020.semeval-1.282
%U https://aclanthology.org/2020.semeval-1.282/
%U https://doi.org/10.18653/v1/2020.semeval-1.282
%P 2123-2132
Markdown (Informal)
[problemConquero at SemEval-2020 Task 12: Transformer and Soft Label-based Approaches](https://aclanthology.org/2020.semeval-1.282/) (Laud et al., SemEval 2020)
ACL