论文标题
Fissa在Semeval-2020任务9：对感情进行微调
FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings
论文作者
论文摘要
在本文中，我们在Semeval-2020任务9中介绍了对西班牙语代码混合社交媒体数据的情感分类方法。我们通过使用不同的微调策略来研究各种预训练的变压器模型的性能。我们使用标准的微调方法探索单语和多语言模型。此外，我们提出了一个自定义模型，该模型我们以两个步骤进行微调：一次具有语言建模目标，并且一次具有特定于任务的目标。尽管两步微调可以改善基本模型的情感分类表现，但大型多语言XLM-Roberta模型在开发数据上以0.537的速度达到了最佳加权F1分数和0.739的测试数据。有了这个分数，我们的团队jupitter在比赛中排名第十。
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.
