FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings

• URL: http://arxiv.org/abs/2007.12544v3
• Date: Mon, 19 Oct 2020 07:11:18 GMT
• Title: FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings
• Authors: Bertelt Braaksma, Richard Scholtens, Stan van Suijlekom, Remy Wang, Ahmet \"Ust\"un
• Abstract summary: In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data. We explore both monolingual and multilingual models with the standard fine-tuning method. Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score.
• Score: 2.362412515574206
• License: http://creativecommons.org/licenses/by/4.0/
• 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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