Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved impressive results on various natural language processing tasks such as question-answering and natural language inference. In this paper, we describe a simple re-implementation of BERT for query-based passage re-ranking. Our system is the state of the art on the TREC-CAR dataset and the top entry in the leaderboard of the MS MARCO passage retrieval task, outperforming the previous state of the art by 27% (relative) in MRR@10. The code to reproduce our results is available at https://github.com/nyu-dl/dl4marco-bert
翻译：最近,在语言模拟任务,如ELMO(Peters等人,2017年)、OpenAI GPT(Radford等人,2018年)和BERT(Devlin等人,2018年)等语言模型培训前的神经模型,在诸如问答和自然语言推断等各种自然语言处理任务方面取得了令人印象深刻的成果。本文描述了对基于查询的通过重新排级的BERT的简单重新实施。我们的系统是TREC-CAR数据集的先进程度和MS MARCO通道检索任务首版的顶尖条目,比MRR@10中以往的艺术状态高27%(相对性)。 复制我们结果的代码可以在 https://github.com/nyu-dl/dl4marco-bert上查阅。
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