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

中文翻译：

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用于 MS MARCO 文档重新排序任务的 Longformer

两步文档排序，其中初始检索通过经典信息检索方法完成，然后是神经重新排序模型，是新标准。最好的性能是通过使用基于转换器的模型作为重新排序器来实现的，例如 BERT。我们在 MS MARCO 文档重新排序任务中采用了 Longformer，这是一种类似于 BERT 的长文档模型。可以在以下位置找到用于训练模型的完整代码：https://github.com/isekulic/longformer-marco