Search Results for author: Christopher Sciavolino

Found 2 papers, 1 papers with code

Towards Universal Dense Retrieval for Open-domain Question Answering

no code implementations23 Sep 2021 Christopher Sciavolino

In this paper, we investigate dense retrieval models in the context of open-domain question answering across different input distributions.

Open-Domain Question Answering Retrieval +2

Simple Entity-Centric Questions Challenge Dense Retrievers

1 code implementation EMNLP 2021 Christopher Sciavolino, Zexuan Zhong, Jinhyuk Lee, Danqi Chen

Open-domain question answering has exploded in popularity recently due to the success of dense retrieval models, which have surpassed sparse models using only a few supervised training examples.

Data Augmentation Open-Domain Question Answering +2

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