Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Rodrigo Nogueira
- sl:arxiv_num : 1901.04085
- sl:arxiv_published : 2019-01-13T23:27:58Z
- sl:arxiv_summary : 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@en
- sl:arxiv_title : Passage Re-ranking with BERT@en
- sl:arxiv_updated : 2020-04-14T14:57:40Z
- sl:bookmarkOf : https://arxiv.org/abs/1901.04085
- sl:creationDate : 2021-03-26
- sl:creationTime : 2021-03-26T01:49:42Z
- sl:relatedDoc : http://www.semanlink.net/doc/2021/03/rodrigo_nogueira_sur_twitter_
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