@inproceedings{deka-etal-2022-bert,
title = "{BERT}-based Language Identification in Code-Mix {K}annada-{E}nglish Text at the {C}o{LI}-Kanglish Shared Task@{ICON} 2022",
author = "Deka, Pritam and
Jyoti Kalita, Nayan and
Kumar Sarma, Shikhar",
editor = "Chakravarthi, Bharathi Raja and
Murugappan, Abirami and
Chinnappa, Dhivya and
Hane, Adeep and
Kumeresan, Prasanna Kumar and
Ponnusamy, Rahul",
booktitle = "Proceedings of the 19th International Conference on Natural Language Processing (ICON): Shared Task on Word Level Language Identification in Code-mixed Kannada-English Texts",
month = dec,
year = "2022",
address = "IIIT Delhi, New Delhi, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.icon-wlli.3",
pages = "12--17",
abstract = "Language identification has recently gained research interest in code-mixed languages due to the extensive use of social media among people. People who speak multiple languages tend to use code-mixed languages when communicating with each other. It has become necessary to identify the languages in such code-mixed environment to detect hate speeches, fake news, misinformation or disinformation and for tasks such as sentiment analysis. In this work, we have proposed a BERT-based approach for language identification in the CoLI-Kanglish shared task at ICON 2022. Our approach achieved 86{\%} weighted average F-1 score and a macro average F-1 score of 57{\%} in the test set.",
}
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<abstract>Language identification has recently gained research interest in code-mixed languages due to the extensive use of social media among people. People who speak multiple languages tend to use code-mixed languages when communicating with each other. It has become necessary to identify the languages in such code-mixed environment to detect hate speeches, fake news, misinformation or disinformation and for tasks such as sentiment analysis. In this work, we have proposed a BERT-based approach for language identification in the CoLI-Kanglish shared task at ICON 2022. Our approach achieved 86% weighted average F-1 score and a macro average F-1 score of 57% in the test set.</abstract>
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%0 Conference Proceedings
%T BERT-based Language Identification in Code-Mix Kannada-English Text at the CoLI-Kanglish Shared Task@ICON 2022
%A Deka, Pritam
%A Jyoti Kalita, Nayan
%A Kumar Sarma, Shikhar
%Y Chakravarthi, Bharathi Raja
%Y Murugappan, Abirami
%Y Chinnappa, Dhivya
%Y Hane, Adeep
%Y Kumeresan, Prasanna Kumar
%Y Ponnusamy, Rahul
%S Proceedings of the 19th International Conference on Natural Language Processing (ICON): Shared Task on Word Level Language Identification in Code-mixed Kannada-English Texts
%D 2022
%8 December
%I Association for Computational Linguistics
%C IIIT Delhi, New Delhi, India
%F deka-etal-2022-bert
%X Language identification has recently gained research interest in code-mixed languages due to the extensive use of social media among people. People who speak multiple languages tend to use code-mixed languages when communicating with each other. It has become necessary to identify the languages in such code-mixed environment to detect hate speeches, fake news, misinformation or disinformation and for tasks such as sentiment analysis. In this work, we have proposed a BERT-based approach for language identification in the CoLI-Kanglish shared task at ICON 2022. Our approach achieved 86% weighted average F-1 score and a macro average F-1 score of 57% in the test set.
%U https://aclanthology.org/2022.icon-wlli.3
%P 12-17
Markdown (Informal)
[BERT-based Language Identification in Code-Mix Kannada-English Text at the CoLI-Kanglish Shared Task@ICON 2022](https://aclanthology.org/2022.icon-wlli.3) (Deka et al., ICON 2022)
ACL