@inproceedings{el-balima-cordero-etal-2023-i2c,
title = "{I}2{C} {H}uelva at {S}em{E}val-2023 Task 4: A Resampling and Transformers Approach to Identify Human Values behind Arguments",
author = "El Balima Cordero, Nordin and
Mata V{\'a}zquez, Jacinto and
Pach{\'o}n {\'A}lvarez, Victoria and
Pichardo Estevez, Abel",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.191",
doi = "10.18653/v1/2023.semeval-1.191",
pages = "1382--1387",
abstract = "This paper presents the approaches proposedfor I2C Group to address the SemEval-2023Task 4: Identification of Human Values behindArguments (ValueEval){''}, whose goal is to classify 20 different categories of human valuesgiven a textual argument. The dataset of thistask consists of one argument per line, including its unique argument ID, conclusion, stanceof the premise towards the conclusion and thepremise text. To indicate whether the argumentdraws or not on that category a binary indication (1 or 0) is included. Participants can submit approaches that detect one, multiple, or allof these values in arguments. The task providesan opportunity for researchers to explore theuse of automated techniques to identify humanvalues in text and has potential applications invarious domains such as social science, politics,and marketing. To deal with the imbalancedclass distribution given, our approach undersamples the data. Additionally, the three components of the argument (conclusion, stanceand premise) are used for training. The systemoutperformed the BERT baseline according toofficial evaluation metrics, achieving a f1 scoreof 0.46.",
}
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<abstract>This paper presents the approaches proposedfor I2C Group to address the SemEval-2023Task 4: Identification of Human Values behindArguments (ValueEval)”, whose goal is to classify 20 different categories of human valuesgiven a textual argument. The dataset of thistask consists of one argument per line, including its unique argument ID, conclusion, stanceof the premise towards the conclusion and thepremise text. To indicate whether the argumentdraws or not on that category a binary indication (1 or 0) is included. Participants can submit approaches that detect one, multiple, or allof these values in arguments. The task providesan opportunity for researchers to explore theuse of automated techniques to identify humanvalues in text and has potential applications invarious domains such as social science, politics,and marketing. To deal with the imbalancedclass distribution given, our approach undersamples the data. Additionally, the three components of the argument (conclusion, stanceand premise) are used for training. The systemoutperformed the BERT baseline according toofficial evaluation metrics, achieving a f1 scoreof 0.46.</abstract>
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%0 Conference Proceedings
%T I2C Huelva at SemEval-2023 Task 4: A Resampling and Transformers Approach to Identify Human Values behind Arguments
%A El Balima Cordero, Nordin
%A Mata Vázquez, Jacinto
%A Pachón Álvarez, Victoria
%A Pichardo Estevez, Abel
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F el-balima-cordero-etal-2023-i2c
%X This paper presents the approaches proposedfor I2C Group to address the SemEval-2023Task 4: Identification of Human Values behindArguments (ValueEval)”, whose goal is to classify 20 different categories of human valuesgiven a textual argument. The dataset of thistask consists of one argument per line, including its unique argument ID, conclusion, stanceof the premise towards the conclusion and thepremise text. To indicate whether the argumentdraws or not on that category a binary indication (1 or 0) is included. Participants can submit approaches that detect one, multiple, or allof these values in arguments. The task providesan opportunity for researchers to explore theuse of automated techniques to identify humanvalues in text and has potential applications invarious domains such as social science, politics,and marketing. To deal with the imbalancedclass distribution given, our approach undersamples the data. Additionally, the three components of the argument (conclusion, stanceand premise) are used for training. The systemoutperformed the BERT baseline according toofficial evaluation metrics, achieving a f1 scoreof 0.46.
%R 10.18653/v1/2023.semeval-1.191
%U https://aclanthology.org/2023.semeval-1.191
%U https://doi.org/10.18653/v1/2023.semeval-1.191
%P 1382-1387
Markdown (Informal)
[I2C Huelva at SemEval-2023 Task 4: A Resampling and Transformers Approach to Identify Human Values behind Arguments](https://aclanthology.org/2023.semeval-1.191) (El Balima Cordero et al., SemEval 2023)
ACL