@inproceedings{prasad-etal-2022-gjg,
title = "{GJG}@{T}amil{NLP}-{ACL}2022: Emotion Analysis and Classification in {T}amil using Transformers",
author = "Prasad, Janvi and
Prasad, Gaurang and
C, Gunavathi",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Krishnamurthy, Parameswari and
Sherly, Elizabeth and
Mahesan, Sinnathamby",
booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dravidianlangtech-1.14",
doi = "10.18653/v1/2022.dravidianlangtech-1.14",
pages = "86--92",
abstract = "This paper describes the systems built by our team for the {``}Emotion Analysis in Tamil{''} shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. There were two multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A contained 11 types of emotions while sub-task B was more fine-grained with 31 emotions. We fine-tuned an XLM-RoBERTa and DeBERTA base model for each sub-task. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.46 and the DeBERTa model achieved an accuracy of 0.45. We had the best classification performance out of 11 teams for sub-task A. For sub-task B, the XLM-RoBERTa model{'}s accuracy was 0.33 and the DeBERTa model had an accuracy of 0.26. We ranked 2nd out of 7 teams for sub-task B.",
}
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<abstract>This paper describes the systems built by our team for the “Emotion Analysis in Tamil” shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. There were two multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A contained 11 types of emotions while sub-task B was more fine-grained with 31 emotions. We fine-tuned an XLM-RoBERTa and DeBERTA base model for each sub-task. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.46 and the DeBERTa model achieved an accuracy of 0.45. We had the best classification performance out of 11 teams for sub-task A. For sub-task B, the XLM-RoBERTa model’s accuracy was 0.33 and the DeBERTa model had an accuracy of 0.26. We ranked 2nd out of 7 teams for sub-task B.</abstract>
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%0 Conference Proceedings
%T GJG@TamilNLP-ACL2022: Emotion Analysis and Classification in Tamil using Transformers
%A Prasad, Janvi
%A Prasad, Gaurang
%A C, Gunavathi
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%Y Mahesan, Sinnathamby
%S Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F prasad-etal-2022-gjg
%X This paper describes the systems built by our team for the “Emotion Analysis in Tamil” shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. There were two multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A contained 11 types of emotions while sub-task B was more fine-grained with 31 emotions. We fine-tuned an XLM-RoBERTa and DeBERTA base model for each sub-task. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.46 and the DeBERTa model achieved an accuracy of 0.45. We had the best classification performance out of 11 teams for sub-task A. For sub-task B, the XLM-RoBERTa model’s accuracy was 0.33 and the DeBERTa model had an accuracy of 0.26. We ranked 2nd out of 7 teams for sub-task B.
%R 10.18653/v1/2022.dravidianlangtech-1.14
%U https://aclanthology.org/2022.dravidianlangtech-1.14
%U https://doi.org/10.18653/v1/2022.dravidianlangtech-1.14
%P 86-92
Markdown (Informal)
[GJG@TamilNLP-ACL2022: Emotion Analysis and Classification in Tamil using Transformers](https://aclanthology.org/2022.dravidianlangtech-1.14) (Prasad et al., DravidianLangTech 2022)
ACL