@inproceedings{garcia-diaz-etal-2022-umuteam-semeval-2022,
title = "{UMUT}eam at {S}em{E}val-2022 Task 6: Evaluating Transformers for detecting Sarcasm in {E}nglish and {A}rabic",
author = "Garc{\'\i}a-D{\'\i}az, Jos{\'e} and
Caparros-Laiz, Camilo and
Valencia-Garc{\'\i}a, Rafael",
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-1.142",
doi = "10.18653/v1/2022.semeval-1.142",
pages = "1012--1017",
abstract = "In this manuscript we detail the participation of the UMUTeam in the iSarcasm shared task (SemEval-2022). This shared task is related to the identification of sarcasm in English and Arabic documents. Our team achieve in the first challenge, a binary classification task, a F1 score of the sarcastic class of 17.97 for English and 31.75 for Arabic. For the second challenge, a multi-label classification, our results are not recorded due to an unknown problem. Therefore, we report the results of each sarcastic mechanism with the validation split. For our proposal, several neural networks that combine language-independent linguistic features with pre-trained embeddings are trained. The embeddings are based on different schemes, such as word and sentence embeddings, and contextual and non-contextual embeddings. Besides, we evaluate different techniques for the integration of the feature sets, such as ensemble learning and knowledge integration. In general, our best results are achieved using the knowledge integration strategy.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="garcia-diaz-etal-2022-umuteam-semeval-2022">
<titleInfo>
<title>UMUTeam at SemEval-2022 Task 6: Evaluating Transformers for detecting Sarcasm in English and Arabic</title>
</titleInfo>
<name type="personal">
<namePart type="given">José</namePart>
<namePart type="family">García-Díaz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Camilo</namePart>
<namePart type="family">Caparros-Laiz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rafael</namePart>
<namePart type="family">Valencia-García</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Guy</namePart>
<namePart type="family">Emerson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Natalie</namePart>
<namePart type="family">Schluter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gabriel</namePart>
<namePart type="family">Stanovsky</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ritesh</namePart>
<namePart type="family">Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Siddharth</namePart>
<namePart type="family">Singh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shyam</namePart>
<namePart type="family">Ratan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Seattle, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this manuscript we detail the participation of the UMUTeam in the iSarcasm shared task (SemEval-2022). This shared task is related to the identification of sarcasm in English and Arabic documents. Our team achieve in the first challenge, a binary classification task, a F1 score of the sarcastic class of 17.97 for English and 31.75 for Arabic. For the second challenge, a multi-label classification, our results are not recorded due to an unknown problem. Therefore, we report the results of each sarcastic mechanism with the validation split. For our proposal, several neural networks that combine language-independent linguistic features with pre-trained embeddings are trained. The embeddings are based on different schemes, such as word and sentence embeddings, and contextual and non-contextual embeddings. Besides, we evaluate different techniques for the integration of the feature sets, such as ensemble learning and knowledge integration. In general, our best results are achieved using the knowledge integration strategy.</abstract>
<identifier type="citekey">garcia-diaz-etal-2022-umuteam-semeval-2022</identifier>
<identifier type="doi">10.18653/v1/2022.semeval-1.142</identifier>
<location>
<url>https://aclanthology.org/2022.semeval-1.142</url>
</location>
<part>
<date>2022-07</date>
<extent unit="page">
<start>1012</start>
<end>1017</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T UMUTeam at SemEval-2022 Task 6: Evaluating Transformers for detecting Sarcasm in English and Arabic
%A García-Díaz, José
%A Caparros-Laiz, Camilo
%A Valencia-García, Rafael
%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 garcia-diaz-etal-2022-umuteam-semeval-2022
%X In this manuscript we detail the participation of the UMUTeam in the iSarcasm shared task (SemEval-2022). This shared task is related to the identification of sarcasm in English and Arabic documents. Our team achieve in the first challenge, a binary classification task, a F1 score of the sarcastic class of 17.97 for English and 31.75 for Arabic. For the second challenge, a multi-label classification, our results are not recorded due to an unknown problem. Therefore, we report the results of each sarcastic mechanism with the validation split. For our proposal, several neural networks that combine language-independent linguistic features with pre-trained embeddings are trained. The embeddings are based on different schemes, such as word and sentence embeddings, and contextual and non-contextual embeddings. Besides, we evaluate different techniques for the integration of the feature sets, such as ensemble learning and knowledge integration. In general, our best results are achieved using the knowledge integration strategy.
%R 10.18653/v1/2022.semeval-1.142
%U https://aclanthology.org/2022.semeval-1.142
%U https://doi.org/10.18653/v1/2022.semeval-1.142
%P 1012-1017
Markdown (Informal)
[UMUTeam at SemEval-2022 Task 6: Evaluating Transformers for detecting Sarcasm in English and Arabic](https://aclanthology.org/2022.semeval-1.142) (García-Díaz et al., SemEval 2022)
ACL