About
Passage re-ranking is the task of scoring and re-ranking a collection of retrieved documents based on an input query.
Benchmarks
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Greatest papers with code
Document Expansion by Query Prediction
One technique to improve the retrieval effectiveness of a search engine is to expand documents with terms that are related or representative of the documents' content. From the perspective of a question answering system, this might comprise questions the document can potentially answer.
Ranked #1 on
Passage Re-Ranking
on MS MARCO
Passage Re-ranking with BERT
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.
Ranked #2 on
Passage Re-Ranking
on MS MARCO
(using extra training data)
An Updated Duet Model for Passage Re-ranking
We propose several small modifications to Duet---a deep neural ranking model---and evaluate the updated model on the MS MARCO passage ranking task.
Ranked #3 on
Passage Re-Ranking
on MS MARCO
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
• sebastian-hofstaetter/transformer-kernel-ranking
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In this work we analyze position bias on datasets, the contextualized representations, and their effect on retrieval results.