论文标题
提前和微调策略，用于对拉脱维亚推文的情感分析
Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets
论文作者
论文摘要
在本文中，我们提出了各种培训策略，有助于提出情感分类任务的准确性。首先，我们使用这些策略进行了培训前语言表示模型，然后在下游任务上对其进行微调。时间均衡的推文评估的实验结果对先前技术的改进。我们对拉脱维亚推文实现了76％的准确性分析，这对审视前的工作有了很大的改进
In this paper, we present various pre-training strategies that aid in im-proving the accuracy of the sentiment classification task. We, at first, pre-trainlanguage representation models using these strategies and then fine-tune them onthe downstream task. Experimental results on a time-balanced tweet evaluation setshow the improvement over the previous technique. We achieve 76% accuracy forsentiment analysis on Latvian tweets, which is a substantial improvement over pre-vious work
