RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder
Shitao Xiao, Zheng Liu, Yingxia Shao, Zhao Cao · 2022
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/RetroMAEso as to inspire more interesting research.