论文标题
wnut-2020任务2的Covcor20:尝试结合深度学习和专家规则的尝试
COVCOR20 at WNUT-2020 Task 2: An Attempt to Combine Deep Learning and Expert rules
论文作者
论文摘要
在WNUT-2020任务2的范围中,我们使用深度学习模型开发了各种文本分类系统,并使用语言知情的规则开发了一种。尽管两个深度学习系统都使用语言知情的规则优于系统,但我们发现,通过(输出)这三个系统的集成(与在交叉验证设置中的每种方法的独立性能)相比,可以实现更好的性能。但是,在测试数据上,集成的性能略低于我们最佳性能深度学习模型。这些结果几乎不能表明整合机器学习和专家规则驱动系统的任何进展。我们预计,在此研讨会将阐明这些令人困惑的结果后,释放注释手册和测试数据的金标签。
In the scope of WNUT-2020 Task 2, we developed various text classification systems, using deep learning models and one using linguistically informed rules. While both of the deep learning systems outperformed the system using the linguistically informed rules, we found that through the integration of (the output of) the three systems a better performance could be achieved than the standalone performance of each approach in a cross-validation setting. However, on the test data the performance of the integration was slightly lower than our best performing deep learning model. These results hardly indicate any progress in line of integrating machine learning and expert rules driven systems. We expect that the release of the annotation manuals and gold labels of the test data after this workshop will shed light on these perplexing results.