论文标题
联系人：荷兰共同-19改编的BERT，用于疫苗犹豫和论证检测
CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection
论文作者
论文摘要
我们提出联系人：一种适合Covid-19推文领域的荷兰语语言模型。 The model was developed by continuing the pre-training phase of RobBERT (Delobelle, 2020) by using 2.8M Dutch COVID-19 related tweets posted in 2021. In order to test the performance of the model and compare it to RobBERT, the two models were tested on two tasks: (1) binary vaccine hesitancy detection and (2) detection of arguments for vaccine hesitancy.对于这两个任务，不仅Twitter，而且还使用Facebook数据来显示跨这样的性能。在我们的实验中，在所有实验中，接触显示了任务1的所有实验中的统计学意义。对于任务2，我们观察到了所有实验中几乎所有类别的显着改善。错误分析表明，域的适应性产生了域特异性术语的更好表示，从而导致接触以做出更准确的分类决策。
We present CoNTACT: a Dutch language model adapted to the domain of COVID-19 tweets. The model was developed by continuing the pre-training phase of RobBERT (Delobelle, 2020) by using 2.8M Dutch COVID-19 related tweets posted in 2021. In order to test the performance of the model and compare it to RobBERT, the two models were tested on two tasks: (1) binary vaccine hesitancy detection and (2) detection of arguments for vaccine hesitancy. For both tasks, not only Twitter but also Facebook data was used to show cross-genre performance. In our experiments, CoNTACT showed statistically significant gains over RobBERT in all experiments for task 1. For task 2, we observed substantial improvements in virtually all classes in all experiments. An error analysis indicated that the domain adaptation yielded better representations of domain-specific terminology, causing CoNTACT to make more accurate classification decisions.
