论文标题
palomino-ochoa在Semeval-2020任务9：基于变压器的强大系统用于代码混合情感分类
Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification
论文作者
论文摘要
我们提出了一个转移学习系统，以执行混合的西班牙语 - 英语分类任务。我们的建议使用最先进的语言模型BERT并将其嵌入ULMFIT转移学习管道中。这种组合使我们能够预测代码混合（英语 - 西班牙）推文的极性检测。因此，在29个提交的系统中，我们的方法（称为dplominop）在Semeval 2020任务9的Sentimix Spanglish测试集中排名第四。
We present a transfer learning system to perform a mixed Spanish-English sentiment classification task. Our proposal uses the state-of-the-art language model BERT and embed it within a ULMFiT transfer learning pipeline. This combination allows us to predict the polarity detection of code-mixed (English-Spanish) tweets. Thus, among 29 submitted systems, our approach (referred to as dplominop) is ranked 4th on the Sentimix Spanglish test set of SemEval 2020 Task 9. In fact, our system yields the weighted-F1 score value of 0.755 which can be easily reproduced -- the source code and implementation details are made available.
