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% in NDCG@10 and 22% in P@1, the underlying state-of-the-art dense retrieval model.

中文翻译：

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DESIRE-ME：使用专家混合的领域增强监督信息检索

开放域问答要求检索系统能够应对问题的多样性和多样性，并在广泛的查询类型和主题中提供准确的答案。为了通过独特的模型处理这种主题异质性，我们提出了 DESIRE-ME，这是一种神经信息检索模型，利用 Mixture-of-Experts 框架来组合多个专门的神经模型。我们依靠维基百科数据来训练有效的神经门控机制，该机制对传入的查询进行分类，并相应地权衡不同特定领域专家的预测。这使得 DESIRE-ME 能够自适应地专注于多个领域。通过对公开数据集的广泛实验，我们表明我们的建议可以有效地推广领域增强神经模型。 DESIRE-ME 擅长自适应处理开放域问题，在 NDCG@10 中提升高达 12%，在 P@1（底层最先进的密集检索模型）中提升高达 22%。