论文标题
MS MARCO文档重新排行任务的longformer
Longformer for MS MARCO Document Re-ranking Task
论文作者
论文摘要
两步文档排名，其中初始检索是通过经典信息检索方法完成的，其次是神经重新排列模型，是新标准。最好的性能是通过将基于变压器的模型作为重新级别（例如Bert）来实现的。我们在MARCO文档重新列入任务上使用Longformer，这是长期文档的类似于BERT的模型。用于培训的完整代码可以在以下网址找到：https：//github.com/isekulic/longformer-marco
Two step document ranking, where the initial retrieval is done by a classical information retrieval method, followed by neural re-ranking model, is the new standard. The best performance is achieved by using transformer-based models as re-rankers, e.g., BERT. We employ Longformer, a BERT-like model for long documents, on the MS MARCO document re-ranking task. The complete code used for training the model can be found on: https://github.com/isekulic/longformer-marco
