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        <title>PrecogIIITH@WASSA2023: Emotion Detection for Urdu-English Code-mixed Text</title>
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        <namePart type="given">Bhaskara</namePart>
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    <abstract>Code-mixing refers to the phenomenon of using two or more languages interchangeably within a speech or discourse context. This practice is particularly prevalent on social media platforms, and determining the embedded affects in a code-mixed sentence remains as a challenging problem. In this submission we describe our system for WASSA 2023 Shared Task on Emotion Detection in English-Urdu code-mixed text. In our system we implement a multiclass emotion detection model with label space of 11 emotions. Samples are code-mixed English-Urdu text, where Urdu is written in romanised form. Our submission is limited to one of the subtasks - Multi Class classification and we leverage transformer-based Multilingual Large Language Models (MLLMs), XLM-RoBERTa and Indic-BERT. We fine-tune MLLMs on the released data splits, with and without pre-processing steps (translation to english), for classifying texts into the appropriate emotion category. Our methods did not surpass the baseline, and our submission is ranked sixth overall.</abstract>
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%0 Conference Proceedings
%T PrecogIIITH@WASSA2023: Emotion Detection for Urdu-English Code-mixed Text
%A Vedula, Bhaskara Hanuma
%A Kodali, Prashant
%A Shrivastava, Manish
%A Kumaraguru, Ponnurangam
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F vedula-etal-2023-precogiiith
%X Code-mixing refers to the phenomenon of using two or more languages interchangeably within a speech or discourse context. This practice is particularly prevalent on social media platforms, and determining the embedded affects in a code-mixed sentence remains as a challenging problem. In this submission we describe our system for WASSA 2023 Shared Task on Emotion Detection in English-Urdu code-mixed text. In our system we implement a multiclass emotion detection model with label space of 11 emotions. Samples are code-mixed English-Urdu text, where Urdu is written in romanised form. Our submission is limited to one of the subtasks - Multi Class classification and we leverage transformer-based Multilingual Large Language Models (MLLMs), XLM-RoBERTa and Indic-BERT. We fine-tune MLLMs on the released data splits, with and without pre-processing steps (translation to english), for classifying texts into the appropriate emotion category. Our methods did not surpass the baseline, and our submission is ranked sixth overall.
%R 10.18653/v1/2023.wassa-1.58
%U https://aclanthology.org/2023.wassa-1.58
%U https://doi.org/10.18653/v1/2023.wassa-1.58
%P 601-605

Markdown (Informal)

[PrecogIIITH@WASSA2023: Emotion Detection for Urdu-English Code-mixed Text](https://aclanthology.org/2023.wassa-1.58) (Vedula et al., WASSA 2023)

• PrecogIIITH@WASSA2023: Emotion Detection for Urdu-English Code-mixed Text (Vedula et al., WASSA 2023)

ACL

• Bhaskara Hanuma Vedula, Prashant Kodali, Manish Shrivastava, and Ponnurangam Kumaraguru. 2023. PrecogIIITH@WASSA2023: Emotion Detection for Urdu-English Code-mixed Text. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 601–605, Toronto, Canada. Association for Computational Linguistics.