@inproceedings{yenumulapalli-etal-2024-techssn1,
title = "{TECHSSN}1 at {S}em{E}val-2024 Task 10: Emotion Classification in {H}indi-{E}nglish Code-Mixed Dialogue using Transformer-based Models",
author = "Yenumulapalli, Venkatasai Ojus and
Premnath, Pooja and
Mohankumar, Parthiban and
Sivanaiah, Rajalakshmi and
Deborah, Angel",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.119",
doi = "10.18653/v1/2024.semeval-1.119",
pages = "833--838",
abstract = "The increase in the popularity of code mixed languages has resulted in the need to engineer language models for the same . Unlike pure languages, code-mixed languages lack clear grammatical structures, leading to ambiguous sentence constructions. This ambiguity presents significant challenges for natural language processing tasks, including syntactic parsing, word sense disambiguation, and language identification. This paper focuses on emotion recognition of conversations in Hinglish, a mix of Hindi and English, as part of Task 10 of SemEval 2024. The proposed approach explores the usage of standard machine learning models like SVM, MNB and RF, and also BERT-based models for Hindi-English code-mixed data- namely, HingBERT, Hing mBERT and HingRoBERTa for subtask A.",
}
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%0 Conference Proceedings
%T TECHSSN1 at SemEval-2024 Task 10: Emotion Classification in Hindi-English Code-Mixed Dialogue using Transformer-based Models
%A Yenumulapalli, Venkatasai Ojus
%A Premnath, Pooja
%A Mohankumar, Parthiban
%A Sivanaiah, Rajalakshmi
%A Deborah, Angel
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F yenumulapalli-etal-2024-techssn1
%X The increase in the popularity of code mixed languages has resulted in the need to engineer language models for the same . Unlike pure languages, code-mixed languages lack clear grammatical structures, leading to ambiguous sentence constructions. This ambiguity presents significant challenges for natural language processing tasks, including syntactic parsing, word sense disambiguation, and language identification. This paper focuses on emotion recognition of conversations in Hinglish, a mix of Hindi and English, as part of Task 10 of SemEval 2024. The proposed approach explores the usage of standard machine learning models like SVM, MNB and RF, and also BERT-based models for Hindi-English code-mixed data- namely, HingBERT, Hing mBERT and HingRoBERTa for subtask A.
%R 10.18653/v1/2024.semeval-1.119
%U https://aclanthology.org/2024.semeval-1.119
%U https://doi.org/10.18653/v1/2024.semeval-1.119
%P 833-838
Markdown (Informal)
[TECHSSN1 at SemEval-2024 Task 10: Emotion Classification in Hindi-English Code-Mixed Dialogue using Transformer-based Models](https://aclanthology.org/2024.semeval-1.119) (Yenumulapalli et al., SemEval 2024)
ACL