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Passage Re-ranking with BERT - 2019

Research Area:  Machine Learning


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.

Author(s) Name:  Rodrigo Nogueira, Kyunghyun Cho

Journal name:  Computer Science

Conferrence name:  

Publisher name:  arXiv:1901.04085

DOI:  10.48550/arXiv.1901.04085

Volume Information: