论文标题
Emotiongif-yankee：具有基于强大模型的合奏方法的情感分类器
EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods
论文作者
论文摘要
本文提供了一种将基于强大模型的集合方法分类的方法。我们预处理推文数据以增强令牌的覆盖范围。为了减少域偏差，我们首先将推文数据集用于预训练的语言模型。此外，每个分类器都有其优势和劣势，我们利用合奏方法来利用不同类型的模型：平均加权总和。从实验中，我们表明我们的方法对情感分类产生了积极的影响。我们的系统在2020年社交NLP EmotionGif竞赛评估中排名第三。
This paper provides a method to classify sentiment with robust model based ensemble methods. We preprocess tweet data to enhance coverage of tokenizer. To reduce domain bias, we first train tweet dataset for pre-trained language model. Besides, each classifier has its strengths and weakness, we leverage different types of models with ensemble methods: average and power weighted sum. From the experiments, we show that our approach has achieved positive effect for sentiment classification. Our system reached third place among 26 teams from the evaluation in SocialNLP 2020 EmotionGIF competition.
