@inproceedings{sultan-etal-2020-wessa,
title = "{WESSA} at {S}em{E}val-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers",
author = "Sultan, Ahmed and
Salim, Mahmoud and
Gaber, Amina and
El Hosary, Islam",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.181",
doi = "10.18653/v1/2020.semeval-1.181",
pages = "1342--1347",
abstract = "In this paper, we describe our system submitted for SemEval 2020 Task 9, Sentiment Analysis for Code-Mixed Social Media Text alongside other experiments. Our best performing system is a Transfer Learning-based model that fine-tunes XLM-RoBERTa, a transformer-based multilingual masked language model, on monolingual English and Spanish data and Spanish-English code-mixed data. Our system outperforms the official task baseline by achieving a 70.1{\%} average F1-Score on the official leaderboard using the test set. For later submissions, our system manages to achieve a 75.9{\%} average F1-Score on the test set using CodaLab username {``}ahmed0sultan{''}.",
}
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%0 Conference Proceedings
%T WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers
%A Sultan, Ahmed
%A Salim, Mahmoud
%A Gaber, Amina
%A El Hosary, Islam
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 dec
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F sultan-etal-2020-wessa
%X In this paper, we describe our system submitted for SemEval 2020 Task 9, Sentiment Analysis for Code-Mixed Social Media Text alongside other experiments. Our best performing system is a Transfer Learning-based model that fine-tunes XLM-RoBERTa, a transformer-based multilingual masked language model, on monolingual English and Spanish data and Spanish-English code-mixed data. Our system outperforms the official task baseline by achieving a 70.1% average F1-Score on the official leaderboard using the test set. For later submissions, our system manages to achieve a 75.9% average F1-Score on the test set using CodaLab username “ahmed0sultan”.
%R 10.18653/v1/2020.semeval-1.181
%U https://aclanthology.org/2020.semeval-1.181
%U https://doi.org/10.18653/v1/2020.semeval-1.181
%P 1342-1347
Markdown (Informal)
[WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers](https://aclanthology.org/2020.semeval-1.181) (Sultan et al., SemEval 2020)
ACL