Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextualized matching signals. Meanwhile, deep LMs can also be leveraged to improve search index, building retrievers with better recall. One would expect a straightforward combination of both in a pipeline to have additive performance gain. In this paper, we discover otherwise and that popular reranker cannot fully exploit the improved retrieval result. We, therefore, propose a Localized Contrastive Estimation (LCE) for training rerankers and demonstrate it significantly improves deep two-stage models.

中文翻译：

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对多阶段检索管道中的BERT Rerankers进行重新思考培训

预先训练的深度语言模型（LM）改进了文本检索的最新技术。从深层LM进行微调的重排程序基于丰富的上下文匹配信号来估计候选者相关性。同时，还可以利用深层LM来改善搜索索引，以提高召回率。人们会期望管道中两者的直接组合可以增加性能。在本文中，我们发现否则，流行的reranker无法充分利用改进的检索结果。因此，我们提出了一个局部对比估计（LCE）来训练重新排名者，并证明它可以显着改善深层次的两阶段模型。