@inproceedings{takahashi-2024-hidetsune-semeval-2024,
title = "Hidetsune at {S}em{E}val-2024 Task 10: An {E}nglish Based Approach to Emotion Recognition in {H}indi-{E}nglish code-mixed Conversations Using Machine Learning and Machine Translation",
author = "Takahashi, Hidetsune",
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-1.58",
doi = "10.18653/v1/2024.semeval-1.58",
pages = "374--378",
abstract = "In this system paper for SemEval-2024 Task10 subtask 1 (ERC), I present my approach torecognizing emotions in Hindi-English codemixed conversations. I train a SpaCy modelwith English translated data and classify emotions behind Hindi-English code-mixed utterances by using the model and translating theminto English. I use machine translation to translate all the data in Hindi-English mixed language into English due to an easy access to existing data for emotion recognition in English.Some additional data in English are used to enhance my model. This English based approachdemonstrates a fundamental possibility and potential of simplifying code-mixed language intoone major language for emotion recognition.",
}
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<abstract>In this system paper for SemEval-2024 Task10 subtask 1 (ERC), I present my approach torecognizing emotions in Hindi-English codemixed conversations. I train a SpaCy modelwith English translated data and classify emotions behind Hindi-English code-mixed utterances by using the model and translating theminto English. I use machine translation to translate all the data in Hindi-English mixed language into English due to an easy access to existing data for emotion recognition in English.Some additional data in English are used to enhance my model. This English based approachdemonstrates a fundamental possibility and potential of simplifying code-mixed language intoone major language for emotion recognition.</abstract>
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%0 Conference Proceedings
%T Hidetsune at SemEval-2024 Task 10: An English Based Approach to Emotion Recognition in Hindi-English code-mixed Conversations Using Machine Learning and Machine Translation
%A Takahashi, Hidetsune
%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 takahashi-2024-hidetsune-semeval-2024
%X In this system paper for SemEval-2024 Task10 subtask 1 (ERC), I present my approach torecognizing emotions in Hindi-English codemixed conversations. I train a SpaCy modelwith English translated data and classify emotions behind Hindi-English code-mixed utterances by using the model and translating theminto English. I use machine translation to translate all the data in Hindi-English mixed language into English due to an easy access to existing data for emotion recognition in English.Some additional data in English are used to enhance my model. This English based approachdemonstrates a fundamental possibility and potential of simplifying code-mixed language intoone major language for emotion recognition.
%R 10.18653/v1/2024.semeval-1.58
%U https://aclanthology.org/2024.semeval-1.58
%U https://doi.org/10.18653/v1/2024.semeval-1.58
%P 374-378
Markdown (Informal)
[Hidetsune at SemEval-2024 Task 10: An English Based Approach to Emotion Recognition in Hindi-English code-mixed Conversations Using Machine Learning and Machine Translation](https://aclanthology.org/2024.semeval-1.58) (Takahashi, SemEval 2024)
ACL