RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder

Shitao Xiao, Zheng Liu, Yingxia Shao, Zhao Cao


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.
Anthology ID:
2022.emnlp-main.35
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
538–548
Language:
URL:
https://aclanthology.org/2022.emnlp-main.35
DOI:
Bibkey:
Cite (ACL):
Shitao Xiao, Zheng Liu, Yingxia Shao, and Zhao Cao. 2022. RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 538–548, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder (Xiao et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.35.pdf