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    <titleInfo>
        <title>Elevating Code-mixed Text Handling through Auditory Information of Words</title>
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        <namePart type="given">Zishan</namePart>
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    <abstract>With the growing popularity of code-mixed data, there is an increasing need for better handling of this type of data, which poses a number of challenges, such as dealing with spelling variations, multiple languages, different scripts, and a lack of resources. Current language models face difficulty in effectively handling code-mixed data as they primarily focus on the semantic representation of words and ignore the auditory phonetic features. This leads to difficulties in handling spelling variations in code-mixed text. In this paper, we propose an effective approach for creating language models for handling code-mixed textual data using auditory information of words from SOUNDEX. Our approach includes a pre-training step based on masked-language-modelling, which includes SOUNDEX representations (SAMLM) and a new method of providing input data to the pre-trained model. Through experimentation on various code-mixed datasets (of different languages) for sentiment, offensive and aggression classification tasks, we establish that our novel language modeling approach (SAMLM) results in improved robustness towards adversarial attacks on code-mixed classification tasks. Additionally, our SAMLM based approach also results in better classification results over the popular baselines for code-mixed tasks. We use the explainability technique, SHAP (SHapley Additive exPlanations) to explain how the auditory features incorporated through SAMLM assist the model to handle the code-mixed text effectively and increase robustness against adversarial attacks.</abstract>
    <identifier type="citekey">mamta-etal-2023-elevating</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-main.987</identifier>
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        <date>2023-12</date>
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            <start>15918</start>
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%0 Conference Proceedings
%T Elevating Code-mixed Text Handling through Auditory Information of Words
%A Mamta, Mamta
%A Ahmad, Zishan
%A Ekbal, Asif
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F mamta-etal-2023-elevating
%X With the growing popularity of code-mixed data, there is an increasing need for better handling of this type of data, which poses a number of challenges, such as dealing with spelling variations, multiple languages, different scripts, and a lack of resources. Current language models face difficulty in effectively handling code-mixed data as they primarily focus on the semantic representation of words and ignore the auditory phonetic features. This leads to difficulties in handling spelling variations in code-mixed text. In this paper, we propose an effective approach for creating language models for handling code-mixed textual data using auditory information of words from SOUNDEX. Our approach includes a pre-training step based on masked-language-modelling, which includes SOUNDEX representations (SAMLM) and a new method of providing input data to the pre-trained model. Through experimentation on various code-mixed datasets (of different languages) for sentiment, offensive and aggression classification tasks, we establish that our novel language modeling approach (SAMLM) results in improved robustness towards adversarial attacks on code-mixed classification tasks. Additionally, our SAMLM based approach also results in better classification results over the popular baselines for code-mixed tasks. We use the explainability technique, SHAP (SHapley Additive exPlanations) to explain how the auditory features incorporated through SAMLM assist the model to handle the code-mixed text effectively and increase robustness against adversarial attacks.
%R 10.18653/v1/2023.emnlp-main.987
%U https://aclanthology.org/2023.emnlp-main.987
%U https://doi.org/10.18653/v1/2023.emnlp-main.987
%P 15918-15932

Markdown (Informal)

[Elevating Code-mixed Text Handling through Auditory Information of Words](https://aclanthology.org/2023.emnlp-main.987) (Mamta et al., EMNLP 2023)

• Elevating Code-mixed Text Handling through Auditory Information of Words (Mamta et al., EMNLP 2023)

ACL

• Mamta Mamta, Zishan Ahmad, and Asif Ekbal. 2023. Elevating Code-mixed Text Handling through Auditory Information of Words. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15918–15932, Singapore. Association for Computational Linguistics.