Neural document retrievers, including dense passage retrieval (DPR), have outperformed classical lexical-matching retrievers, such as BM25, when fine-tuned and tested on specific question-answering datasets. However, it has been shown that the existing dense retrievers do not generalize well not only out of domain but even in domain such as Wikipedia, especially when a named entity in a question is a dominant clue for retrieval. In this paper, we propose an approach toward in-domain generalization using the embeddings generated by the frozen language model trained with the entities in the domain. By not fine-tuning, we explore the possibility that the rich knowledge contained in a pretrained language model can be used for retrieval tasks. The proposed method outperforms conventional DPRs on entity-centric questions in Wikipedia domain and achieves almost comparable performance to BM25 and state-of-the-art SPAR model. We also show that the contextualized keys lead to strong improvements compared to BM25 when the entity names consist of common words. Our results demonstrate the feasibility of the zero-shot retrieval method for entity-centric questions of Wikipedia domain, where DPR has struggled to perform.

翻译：神经文档检索器，包括密集段落检索（DPR），在特定问答数据集上进行微调和测试时，其性能已超越经典词汇匹配检索器（如BM25）。然而，现有密集检索器不仅跨领域泛化能力不佳，甚至在同一领域（如维基百科）内也表现欠佳，尤其是当问题中的命名实体成为检索主导线索时。本文提出一种方法，利用由该领域实体训练生成的冻结语言模型嵌入来实现领域内泛化。通过避免微调，我们探索了预训练语言模型所含丰富知识用于检索任务的可能性。所提方法在维基百科领域面向实体问题上的表现优于传统DPR，且性能几乎与BM25及当前最先进的SPAR模型相当。我们还证明，当实体名称由常见词汇构成时，上下文相关的键相比BM25能带来显著提升。实验结果展示了在DPR表现欠佳的维基百科领域，针对实体问题的零样本检索方法的可行性。