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
@article{arxiv.2009.09392,
title = {Longformer for MS MARCO Document Re-ranking Task},
author = {Ivan Sekulić and Amir Soleimani and Mohammad Aliannejadi and Fabio Crestani},
journal= {arXiv preprint arXiv:2009.09392},
year = {2020}
}