论文标题
test_positive w-nut 2020共享任务3：噪音填充插槽填充的联合事件多任务学习
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text
论文作者
论文摘要
从Twitter提取Covid-19事件的竞争是开发可以自动从推文中提取相关事件的系统。为了回答重要问题，构建的系统应确定每个事件的不同预定插槽（例如，谁对阳性进行了测试？该人的年龄是多少？他/她在哪里？）。为了应对这些挑战，我们提出了联合活动多任务学习（Joelin）模型。通过统一的全球学习框架，我们利用各种事件中的所有培训数据来学习和调整语言模型。此外，我们使用命名实体识别（NER）实现一种类型感知的后处理过程，以进一步过滤预测。 Joelin在Micro F1中的表现优于Bert基线17.2％。
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Where is he/she?). To tackle these challenges, we propose the Joint Event Multi-task Learning (JOELIN) model. Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model. Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions. JOELIN outperforms the BERT baseline by 17.2% in micro F1.
