Information Retrieval has a long history of applying either dis- criminative or generative modeling to retrieval and ranking tasks. Recent developments in transformer architectures and multi-task learning techniques have dramatically improved our ability to train effective neural models capable of resolving a wide variety of tasks using either of these paradigms. In this paper, we propose a novel multi-task learning approach which can be used to produce more effective neural ranking models. The key idea is to improve the quality of the underlying transformer model by cross-training a retrieval task and one or more complimentary language genera- tion tasks. By targeting the training on the encoding layer in the transformer architecture, our experimental results show that the proposed multi-task learning approach consistently improves re- trieval effectiveness on the targeted collection and can easily be retargeted to new ranking tasks. We provide an in-depth analy- sis showing how multi-task learning modifies model behaviors, resulting in more general models.

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