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Designing pre-training objectives that more closely resemble the downstreamtasks for pre-trained language models can lead to better performance at thefine-tuning stage, especially in the ad-hoc retrieval area. Existingpre-training approaches tailored for IR tried to incorporate weak supervisedsignals, such as query-likelihood based sampling, to construct pseudoquery-document pairs from the raw textual corpus. However, these signals relyheavily on the sampling method. For example, the query likelihood model maylead to much noise in the constructed pre-training data. \blfootnote{$\dagger$This work was done during an internship at Huawei.} In this paper, we proposeto leverage the large-scale hyperlinks and anchor texts to pre-train thelanguage model for ad-hoc retrieval. Since the anchor texts are created bywebmasters and can usually summarize the target document, it can help to buildmore accurate and reliable pre-training samples than a specific algorithm.Considering different views of the downstream ad-hoc retrieval, we devise fourpre-training tasks based on the hyperlinks. We then pre-train the Transformermodel to predict the pair-wise preference, jointly with the Masked LanguageModel objective. Experimental results on two large-scale ad-hoc retrievaldatasets show the significant improvement of our model compared with theexisting methods.

Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need