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

ABSTRACT: Continual learning refers to the capability of a machine learning model to
learn and adapt to new information, without compromising its performance on
previously learned tasks. Although several studies have investigated continual
learning methods for information retrieval tasks, a well-defined task
formulation is still lacking, and it is unclear how typical learning strategies
perform in this context. To address this challenge, a systematic task
formulation of continual neural information retrieval is presented, along with
a multiple-topic dataset that simulates continuous information retrieval. A
comprehensive continual neural information retrieval framework consisting of
typical retrieval models and continual learning strategies is then proposed.
Empirical evaluations illustrate that the proposed framework can successfully
prevent catastrophic forgetting in neural information retrieval and enhance
performance on previously learned tasks. The results indicate that
embedding-based retrieval models experience a decline in their continual
learning performance as the topic shift distance and dataset volume of new
tasks increase. In contrast, pretraining-based models do not show any such
correlation. Adopting suitable learning strategies can mitigate the effects of
topic shift and data augmentation.
Continual learning refers to the capability of a machine learning model to
learn and adapt to new information, without compromising its performance on
previously learned tasks. Although several studies have investigated continual
learning methods for information retrieval tasks, a well-defined task
formulation is still lacking, and it is unclear how typical learning strategies
perform in this context. To address this challenge, a systematic task
formulation of continual neural information retrieval is presented, along with
a multiple-topic dataset that simulates continuous information retrieval. A
comprehensive continual neural information retrieval framework consisting of
typical retrieval models and continual learning strategies is then proposed.
Empirical evaluations illustrate that the proposed framework can successfully
prevent catastrophic forgetting in neural information retrieval and enhance
performance on previously learned tasks. The results indicate that
embedding-based retrieval models experience a decline in their continual
learning performance as the topic shift distance and dataset volume of new
tasks increase. In contrast, pretraining-based models do not show any such
correlation. Adopting suitable learning strategies can mitigate the effects of
topic shift and data augmentation.
Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation
http://arxiv.org/abs/2308.08378v1