论文标题
华盛顿大学TREC 2020公平排名
University of Washington at TREC 2020 Fairness Ranking Track
论文作者
论文摘要
华盛顿大学的Infoseeking Lab的命运（公平责任透明度伦理）小组参加了2020年TREC公平排名。该报告描述了轨道，分配的数据和任务，我们的组定义以及我们的结果。我们通过语义学者数据实现检索和重新排列任务的公平性的方法是提取作者身份的各个方面。这些维度包括性别和位置。我们为这些提取的模块开发了一种方式，使我们可以根据需要将它们插入其中的任何一个任务。在尝试了分配给相关性，性别和位置信息的相对权重的不同组合之后，我们选择了五次跑步进行检索，并进行了五次运行以进行重新级别的任务。结果表明，我们的跑步以低于标准杆的重新排列任务，但要检索的平均水平高于平均水平。
InfoSeeking Lab's FATE (Fairness Accountability Transparency Ethics) group at University of Washington participated in 2020 TREC Fairness Ranking Track. This report describes that track, assigned data and tasks, our group definitions, and our results. Our approach to bringing fairness in retrieval and re-ranking tasks with Semantic Scholar data was to extract various dimensions of author identity. These dimensions included gender and location. We developed modules for these extractions in a way that allowed us to plug them in for either of the tasks as needed. After trying different combinations of relative weights assigned to relevance, gender, and location information, we chose five runs for retrieval and five runs for re-ranking tasks. The results showed that our runs performed below par for re-ranking task, but above average for retrieval.
