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Continual learning refers to the capability of a machine learning model tolearn and adapt to new information, without compromising its performance onpreviously learned tasks. Although several studies have investigated continuallearning methods for information retrieval tasks, a well-defined taskformulation is still lacking, and it is unclear how typical learning strategiesperform in this context. To address this challenge, a systematic taskformulation of continual neural information retrieval is presented, along witha multiple-topic dataset that simulates continuous information retrieval. Acomprehensive continual neural information retrieval framework consisting oftypical retrieval models and continual learning strategies is then proposed.Empirical evaluations illustrate that the proposed framework can successfullyprevent catastrophic forgetting in neural information retrieval and enhanceperformance on previously learned tasks. The results indicate thatembedding-based retrieval models experience a decline in their continuallearning performance as the topic shift distance and dataset volume of newtasks increase. In contrast, pretraining-based models do not show any suchcorrelation. Adopting suitable learning strategies can mitigate the effects oftopic shift and data augmentation.

Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation