Enhanced Retrieval Effectiveness through Selective Query Generation

Prior research has demonstrated that reformulation of queries can significantly enhance retrieval effectiveness. Despite notable successes in neural-based query reformulation methods, identifying optimal reformulations that cover the same information need while enhancing retrieval effectiveness is still challenging. This paper introduces a two-step query reformulation framework for generating and selecting optimal target query variants which not only achieve higher retrieval performance but also preserve the original query's information need. Our comprehensive evaluations on the MS MARCO dataset and TREC Deep Learning tracks demonstrate substantial improvements over original query's performance.