DESIRE-ME: Domain-Enhanced Supervised Information REtrieval using Mixture-of-Experts
Lecture Notes in Computer Science Advances in Information Retrieval(2024)
摘要
Open-domain question answering requires retrieval systems able to cope with
the diverse and varied nature of questions, providing accurate answers across a
broad spectrum of query types and topics. To deal with such topic heterogeneity
through a unique model, we propose DESIRE-ME, a neural information retrieval
model that leverages the Mixture-of-Experts framework to combine multiple
specialized neural models. We rely on Wikipedia data to train an effective
neural gating mechanism that classifies the incoming query and that weighs the
predictions of the different domain-specific experts correspondingly. This
allows DESIRE-ME to specialize adaptively in multiple domains. Through
extensive experiments on publicly available datasets, we show that our proposal
can effectively generalize domain-enhanced neural models. DESIRE-ME excels in
handling open-domain questions adaptively, boosting by up to 12
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