论文标题
WESSA在2020年Semeval-20任务9：使用变压器的代码混合情绪分析
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers
论文作者
论文摘要
在本文中，我们描述了提交给Semeval 2020 Task 9的系统，对代码混合社交媒体文本的情感分析以及其他实验。我们最好的性能系统是基于转移学习的模型，该模型是基于变压器的多语言掩盖语言模型“ XLM-ROBERTA”，介绍了单语英文和西班牙语数据以及西班牙语英语代码混合数据。我们的系统通过使用测试集在官方排行榜上达到70.1％的平均F1得分来优于官方任务基线。对于以后的提交，我们的系统设法使用Codalab用户名“ Ahmed0sultan”在测试集上达到75.9％的F1得分。
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".
