@inproceedings{moreno-monterde-etal-2022-i2c,
title = "{I}2{C} at {S}em{E}val-2022 Task 6: Intended Sarcasm in {E}nglish using Deep Learning Techniques",
author = "Moreno Monterde, Adri{\'a}n and
V{\'a}zquez Ramos, Laura and
Mata, Jacinto and
Pach{\'o}n {\'A}lvarez, Victoria",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.119",
doi = "10.18653/v1/2022.semeval-1.119",
pages = "856--861",
abstract = "Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. This paper describes how the problem proposed in Task 6: Intended Sarcasm Detection in English (Abu Arfa et al. 2022) has been solved. Specifically, we participated in Subtask B: a binary multi-label classification task, where it is necessary to determine whether a tweet belongs to an ironic speech category, if any. Several approaches (classic machine learning and deep learning algorithms) were developed. The final submission consisted of a BERT based model and a macro-F1 score of 0.0699 was obtained.",
}
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%0 Conference Proceedings
%T I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques
%A Moreno Monterde, Adrián
%A Vázquez Ramos, Laura
%A Mata, Jacinto
%A Pachón Álvarez, Victoria
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F moreno-monterde-etal-2022-i2c
%X Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. This paper describes how the problem proposed in Task 6: Intended Sarcasm Detection in English (Abu Arfa et al. 2022) has been solved. Specifically, we participated in Subtask B: a binary multi-label classification task, where it is necessary to determine whether a tweet belongs to an ironic speech category, if any. Several approaches (classic machine learning and deep learning algorithms) were developed. The final submission consisted of a BERT based model and a macro-F1 score of 0.0699 was obtained.
%R 10.18653/v1/2022.semeval-1.119
%U https://aclanthology.org/2022.semeval-1.119
%U https://doi.org/10.18653/v1/2022.semeval-1.119
%P 856-861
Markdown (Informal)
[I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques](https://aclanthology.org/2022.semeval-1.119) (Moreno Monterde et al., SemEval 2022)
ACL