@inproceedings{braaksma-etal-2020-fissa,
title = "{F}i{SSA} at {S}em{E}val-2020 Task 9: Fine-tuned for Feelings",
author = {Braaksma, Bertelt and
Scholtens, Richard and
van Suijlekom, Stan and
Wang, Remy and
{\"U}st{\"u}n, Ahmet},
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-1.165",
doi = "10.18653/v1/2020.semeval-1.165",
pages = "1239--1246",
abstract = "In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data in the SemEval-2020 Task 9. We investigate performance of various pre-trained Transformer models by using different fine-tuning strategies. We explore both monolingual and multilingual models with the standard fine-tuning method. Additionally, we propose a custom model that we fine-tune in two steps: once with a language modeling objective, and once with a task-specific objective. Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score with 0.537 on development data and 0.739 on test data. With this score, our team jupitter placed tenth overall in the competition.",
}
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<abstract>In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data in the SemEval-2020 Task 9. We investigate performance of various pre-trained Transformer models by using different fine-tuning strategies. We explore both monolingual and multilingual models with the standard fine-tuning method. Additionally, we propose a custom model that we fine-tune in two steps: once with a language modeling objective, and once with a task-specific objective. Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score with 0.537 on development data and 0.739 on test data. With this score, our team jupitter placed tenth overall in the competition.</abstract>
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%0 Conference Proceedings
%T FiSSA at SemEval-2020 Task 9: Fine-tuned for Feelings
%A Braaksma, Bertelt
%A Scholtens, Richard
%A van Suijlekom, Stan
%A Wang, Remy
%A Üstün, Ahmet
%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 braaksma-etal-2020-fissa
%X In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data in the SemEval-2020 Task 9. We investigate performance of various pre-trained Transformer models by using different fine-tuning strategies. We explore both monolingual and multilingual models with the standard fine-tuning method. Additionally, we propose a custom model that we fine-tune in two steps: once with a language modeling objective, and once with a task-specific objective. Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score with 0.537 on development data and 0.739 on test data. With this score, our team jupitter placed tenth overall in the competition.
%R 10.18653/v1/2020.semeval-1.165
%U https://aclanthology.org/2020.semeval-1.165
%U https://doi.org/10.18653/v1/2020.semeval-1.165
%P 1239-1246
Markdown (Informal)
[FiSSA at SemEval-2020 Task 9: Fine-tuned for Feelings](https://aclanthology.org/2020.semeval-1.165) (Braaksma et al., SemEval 2020)
ACL
- Bertelt Braaksma, Richard Scholtens, Stan van Suijlekom, Remy Wang, and Ahmet Üstün. 2020. FiSSA at SemEval-2020 Task 9: Fine-tuned for Feelings. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1239–1246, Barcelona (online). International Committee for Computational Linguistics.