Studying Catastrophic Forgetting in Neural Ranking Models
This addresses a practical problem for information retrieval researchers and practitioners by highlighting and mitigating performance degradation in cross-domain neural ranking models, though it is incremental as it builds on existing lifelong learning concepts.
The study investigates catastrophic forgetting in neural ranking models when transferring across domains, finding that effectiveness comes at the cost of forgetting, and a lifelong learning strategy with a cross-domain regularizer successfully mitigates this issue.
Several deep neural ranking models have been proposed in the recent IR literature. While their transferability to one target domain held by a dataset has been widely addressed using traditional domain adaptation strategies, the question of their cross-domain transferability is still under-studied. We study here in what extent neural ranking models catastrophically forget old knowledge acquired from previously observed domains after acquiring new knowledge, leading to performance decrease on those domains. Our experiments show that the effectiveness of neuralIR ranking models is achieved at the cost of catastrophic forgetting and that a lifelong learning strategy using a cross-domain regularizer success-fully mitigates the problem. Using an explanatory approach built on a regression model, we also show the effect of domain characteristics on the rise of catastrophic forgetting. We believe that the obtained results can be useful for both theoretical and practical future work in neural IR.