@inproceedings{verissimo-dos-santos-neto-etal-2020-deep,
title = "Deep Learning Brasil - {NLP} at {S}em{E}val-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models",
author = "Ver{\'\i}ssimo dos Santos Neto, Manoel and
Amaral, Ayrton and
Silva, N{\'a}dia and
da Silva Soares, Anderson",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
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-1.164",
doi = "10.18653/v1/2020.semeval-1.164",
pages = "1233--1238",
abstract = "In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, ALBERT, and XLNET). The final classification algorithm was an ensemble of some predictions of all softmax values from these four models. This architecture was used and evaluated in the context of the SemEval 2020 challenge (task 9), and our system got 72.7{\%} on the F1 score.",
}
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<abstract>In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, ALBERT, and XLNET). The final classification algorithm was an ensemble of some predictions of all softmax values from these four models. This architecture was used and evaluated in the context of the SemEval 2020 challenge (task 9), and our system got 72.7% on the F1 score.</abstract>
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%0 Conference Proceedings
%T Deep Learning Brasil - NLP at SemEval-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models
%A Veríssimo dos Santos Neto, Manoel
%A Amaral, Ayrton
%A Silva, Nádia
%A da Silva Soares, Anderson
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F verissimo-dos-santos-neto-etal-2020-deep
%X In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, ALBERT, and XLNET). The final classification algorithm was an ensemble of some predictions of all softmax values from these four models. This architecture was used and evaluated in the context of the SemEval 2020 challenge (task 9), and our system got 72.7% on the F1 score.
%R 10.18653/v1/2020.semeval-1.164
%U https://aclanthology.org/2020.semeval-1.164
%U https://doi.org/10.18653/v1/2020.semeval-1.164
%P 1233-1238
Markdown (Informal)
[Deep Learning Brasil - NLP at SemEval-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models](https://aclanthology.org/2020.semeval-1.164) (Veríssimo dos Santos Neto et al., SemEval 2020)
ACL