To better support information retrieval tasks such as web search and
open-domain question answering, growing effort is made to develop
retrieval-oriented language models, e.g., RetroMAE and many others. Most of the
existing works focus on improving the semantic representation capability for
the contextualized embedding of the [CLS] token. However, recent study shows
that the ordinary tokens besides [CLS] may provide extra information, which
help to produce a better representation effect. As such, it's necessary to
extend the current methods where all contextualized embeddings can be jointly
pre-trained for the retrieval tasks. In this work, we propose a novel
pre-training method called Duplex Masked Auto-Encoder, a.k.a. DupMAE. It is
designed to improve the quality of semantic representation where all
contextualized embeddings of the pre-trained model can be leveraged. It takes
advantage of two complementary auto-encoding tasks: one reconstructs the input
sentence on top of the [CLS] embedding; the other one predicts the bag-of-words
feature of the input sentence based on the ordinary tokens' embeddings. The two
tasks are jointly conducted to train a unified encoder, where the whole
contextualized embeddings are aggregated in a compact way to produce the final
semantic representation. DupMAE is simple but empirically competitive: it
substantially improves the pre-trained model's representation capability and
transferability, where superior retrieval performances can be achieved on
popular benchmarks, like MS MARCO and BEIR.

本文提出了一种新的预训练方法，名为 Duplex Masked Auto-Encoder（DupMAE），以改善用于信息检索任务的上下文嵌入的语义表示质量，其中所有上下文嵌入都可以被利用。通过两个互补的自编码任务，联合训练一个统一的编码器，实现所有上下文化嵌入的紧凑汇总，生成最终的语义表示。