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
BERT has advanced the state-of-the-art on a variety of NLP tasks, including passage ranking tasks REF .
Passage Re-ranking with BERT
ArXiv
ArXiv
Computer Science