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.

翻译：尽管培训前在许多重要的 NLP 任务中取得了进步,但仍需要探索有效的培训前战略,以便进行密集的检索。在本文中,我们提议进行RetroMAE,这是基于蒙面自动编码器(MAE)的新的检索导向培训前范例。RtroMAE被三个关键设计所突出。1)新的MAE工作流程,其中输入句因编码器和编码器不同面罩而受污染。该句嵌入于编码器掩码器的隐藏输入中;然后,最初的句子根据句子嵌入和解码器的掩码输入,通过遮罩语言建模恢复原句。2)Asymatrial 模型结构,其全面BERT如变压器,其一层变压器为解码器。3) Asymymymeri 掩码比率:15-30%,以及解码器攻击率:50-70%。我们的框架简单化并具有经验竞争力:经过培训的模型通过隐蔽语言建模模型极大地改进了SOTA的性模型,在广泛范围的深层检索中,例如MAR/MARIMA/REcom检索模型。