@inproceedings{xiao-etal-2022-retromae,
    title = "{R}etro{MAE}: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder",
    author = "Xiao, Shitao  and
      Liu, Zheng  and
      Shao, Yingxia  and
      Cao, Zhao",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2022.emnlp-main.35/",
    doi = "10.18653/v1/2022.emnlp-main.35",
    pages = "538--548",
    abstract = "Despite pre-training{'}s progress in many important NLP tasks, it remains to explore effective pre-training strategies for dense retrieval. In this paper, we propose RetroMAE, a new retrieval oriented pre-training paradigm based on Masked Auto-Encoder (MAE). RetroMAE is highlighted by three critical designs. 1) A novel MAE workflow, where the input sentence is polluted for encoder and decoder with different masks. The sentence embedding is generated from the encoder{'}s masked input; then, the original sentence is recovered based on the sentence embedding and the decoder{'}s masked input via masked language modeling. 2) Asymmetric model structure, with a full-scale BERT like transformer as encoder, and a one-layer transformer as decoder. 3) Asymmetric masking ratios, with a moderate ratio for encoder: 15 30{\%}, and an aggressive ratio for decoder: 50 70{\%}. Our framework is simple to realize and empirically competitive: the pre-trained models dramatically improve the SOTA performances on a wide range of dense retrieval benchmarks, like BEIR and MS MARCO. The source code and pre-trained models are made publicly available at https://github.com/staoxiao/RetroMAE so as to inspire more interesting research."
}