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With the development of deep learning and natural language processingtechniques, pre-trained language models have been widely used to solveinformation retrieval (IR) problems. Benefiting from the pre-training andfine-tuning paradigm, these models achieve state-of-the-art performance. Inprevious works, plain texts in Wikipedia have been widely used in thepre-training stage. However, the rich structured information in Wikipedia, suchas the titles, abstracts, hierarchical heading (multi-level title) structure,relationship between articles, references, hyperlink structures, and thewriting organizations, has not been fully explored. In this paper, we devisefour pre-training objectives tailored for IR tasks based on the structuredknowledge of Wikipedia. Compared to existing pre-training methods, our approachcan better capture the semantic knowledge in the training corpus by leveragingthe human-edited structured data from Wikipedia. Experimental results onmultiple IR benchmark datasets show the superior performance of our model inboth zero-shot and fine-tuning settings compared to existing strong retrievalbaselines. Besides, experimental results in biomedical and legal domainsdemonstrate that our approach achieves better performance in vertical domainscompared to previous models, especially in scenarios where long text similaritymatching is needed.

Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval